Object Detection(目标检测神文)
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Object Detection(目標(biāo)檢測(cè)神文)
2018年08月21日 14:25:28
Mars_WH
閱讀數(shù) 23382
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object detect faster R-CNN SSD YOLO MTCNN
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目標(biāo)檢測(cè)
目標(biāo)檢測(cè)神文，非常全而且持續(xù)在更新。轉(zhuǎn)發(fā)自：https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html，如有侵權(quán)聯(lián)系刪除。
更新時(shí)間：
20190226
不再更新，最新檢測(cè)文章請(qǐng)移步：https://blog.csdn.net/hw5226349/article/details/88733364
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文章目錄
Papers損失函數(shù)[CVPR2019] Generalized Intersection over Union: A Metric and A Loss for Bounding Box RegressionDeep Neural Networks for Object DetectionOverFeat: Integrated Recognition, Localization and Detection using Convolutional NetworksR-CNNRich feature hierarchies for accurate object detection and semantic segmentationFast R-CNNFast R-CNNA-Fast-RCNN: Hard Positive Generation via Adversary for Object DetectionFaster R-CNNFaster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksR-CNN minus RFaster R-CNN in MXNet with distributed implementation and data parallelizationContextual Priming and Feedback for Faster R-CNNAn Implementation of Faster RCNN with Study for Region SamplingInterpretable R-CNN[AAAI2019]Object Detection based on Region Decomposition and AssemblyLight-Head R-CNNLight-Head R-CNN: In Defense of Two-Stage Object DetectorCascade R-CNN: Delving into High Quality Object DetectionMultiBoxScalable Object Detection using Deep Neural NetworksScalable, High-Quality Object DetectionSPP-NetSpatial Pyramid Pooling in Deep Convolutional Networks for Visual RecognitionDeepID-Net: Deformable Deep Convolutional Neural Networks for Object DetectionObject Detectors Emerge in Deep Scene CNNssegDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object DetectionObject Detection Networks on Convolutional Feature MapsImproving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured PredictionDeepBox: Learning Objectness with Convolutional NetworksMR-CNNObject detection via a multi-region & semantic segmentation-aware CNN modelYOLOYou Only Look Once: Unified, Real-Time Object Detectiondarkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++Start Training YOLO with Our Own DataYOLO: Core ML versus MPSNNGraphTensorFlow YOLO object detection on AndroidComputer Vision in iOS – Object DetectionYOLOv2YOLO9000: Better, Faster, Strongerdarknet_scriptsYolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2LightNet: Bringing pjreddie’s DarkNet out of the shadowsYOLO v2 Bounding Box ToolYOLOv3YOLOv3: An Incremental ImprovementYOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU ComputersAttentionNet: Aggregating Weak Directions for Accurate Object DetectionDenseBoxDenseBox: Unifying Landmark Localization with End to End Object DetectionSSDSSD: Single Shot MultiBox DetectorDSSDDSSD : Deconvolutional Single Shot DetectorEnhancement of SSD by concatenating feature maps for object detectionContext-aware Single-Shot DetectorFeature-Fused SSD: Fast Detection for Small ObjectsFSSDFSSD: Feature Fusion Single Shot Multibox DetectorWeaving Multi-scale Context for Single Shot DetectorESSDExtend the shallow part of Single Shot MultiBox Detector via Convolutional Neural NetworkTiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object DetectionMDSSD: Multi-scale Deconvolutional Single Shot Detector for small objectsInside-Outside Net (ION)Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural NetworksAdaptive Object Detection Using Adjacency and Zoom PredictionG-CNN: an Iterative Grid Based Object DetectorFactors in Finetuning Deep Model for object detectionFactors in Finetuning Deep Model for Object Detection with Long-tail DistributionWe don’t need no bounding-boxes: Training object class detectors using only human verificationHyperNet: Towards Accurate Region Proposal Generation and Joint Object DetectionA MultiPath Network for Object DetectionCRAFTCRAFT Objects from ImagesOHEMTraining Region-based Object Detectors with Online Hard Example MiningS-OHEM: Stratified Online Hard Example Mining for Object DetectionExploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection ClassifiersR-FCNR-FCN: Object Detection via Region-based Fully Convolutional NetworksR-FCN-3000 at 30fps: Decoupling Detection and ClassificationRecycle deep features for better object detectionMS-CNNA Unified Multi-scale Deep Convolutional Neural Network for Fast Object DetectionMulti-stage Object Detection with Group Recursive LearningSubcategory-aware Convolutional Neural Networks for Object Proposals and DetectionPVANETPVANet: Lightweight Deep Neural Networks for Real-time Object DetectionGBD-NetGated Bi-directional CNN for Object DetectionCrafting GBD-Net for Object DetectionStuffNet: Using ‘Stuff’ to Improve Object DetectionGeneralized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic SceneHierarchical Object Detection with Deep Reinforcement LearningLearning to detect and localize many objects from few examplesSpeed/accuracy trade-offs for modern convolutional object detectorsSqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous DrivingFeature Pyramid Network (FPN)Feature Pyramid Networks for Object DetectionAction-Driven Object Detection with Top-Down Visual AttentionsBeyond Skip Connections: Top-Down Modulation for Object DetectionWide-Residual-Inception Networks for Real-time Object DetectionAttentional Network for Visual Object DetectionLearning Chained Deep Features and Classifiers for Cascade in Object DetectionDeNet: Scalable Real-time Object Detection with Directed Sparse SamplingDiscriminative Bimodal Networks for Visual Localization and Detection with Natural Language QueriesSpatial Memory for Context Reasoning in Object DetectionAccurate Single Stage Detector Using Recurrent Rolling ConvolutionDeep Occlusion Reasoning for Multi-Camera Multi-Target DetectionLCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded SystemsPoint Linking Network for Object DetectionPerceptual Generative Adversarial Networks for Small Object DetectionFew-shot Object DetectionYes-Net: An effective Detector Based on Global InformationSMC Faster R-CNN: Toward a scene-specialized multi-object detectorTowards lightweight convolutional neural networks for object detectionRON: Reverse Connection with Objectness Prior Networks for Object DetectionMimicking Very Efficient Network for Object DetectionResidual Features and Unified Prediction Network for Single Stage DetectionDeformable Part-based Fully Convolutional Network for Object DetectionAdaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object DetectorsRecurrent Scale Approximation for Object Detection in CNNDSODDSOD: Learning Deeply Supervised Object Detectors from ScratchObject Detection from Scratch with Deep SupervisionFocal Loss for Dense Object DetectionFocal Loss Dense Detector for Vehicle SurveillanceCoupleNet: Coupling Global Structure with Local Parts for Object DetectionIncremental Learning of Object Detectors without Catastrophic ForgettingZoom Out-and-In Network with Map Attention Decision for Region Proposal and Object DetectionStairNet: Top-Down Semantic Aggregation for Accurate One Shot DetectionDynamic Zoom-in Network for Fast Object Detection in Large ImagesZero-Annotation Object Detection with Web Knowledge TransferMegDetMegDet: A Large Mini-Batch Object DetectorSingle-Shot Refinement Neural Network for Object DetectionReceptive Field Block Net for Accurate and Fast Object DetectionAn Analysis of Scale Invariance in Object Detection - SNIPFeature Selective Networks for Object DetectionLearning a Rotation Invariant Detector with Rotatable Bounding BoxScalable Object Detection for Stylized ObjectsLearning Object Detectors from Scratch with Gated Recurrent Feature PyramidsDeep Regionlets for Object DetectionTraining and Testing Object Detectors with Virtual ImagesLarge-Scale Object Discovery and Detector Adaptation from Unlabeled VideoSpot the Difference by Object DetectionLocalization-Aware Active Learning for Object DetectionObject Detection with Mask-based Feature EncodingLSTD: A Low-Shot Transfer Detector for Object DetectionDomain Adaptive Faster R-CNN for Object Detection in the WildPseudo Mask Augmented Object DetectionRevisiting RCNN: On Awakening the Classification Power of Faster RCNNDecoupled Classification Refinement: Hard False Positive Suppression for Object DetectionLearning Region Features for Object DetectionSingle-Shot Bidirectional Pyramid Networks for High-Quality Object DetectionObject Detection for Comics using Manga109 AnnotationsTask-Driven Super Resolution: Object Detection in Low-resolution ImagesTransferring Common-Sense Knowledge for Object DetectionMulti-scale Location-aware Kernel Representation for Object DetectionLoss Rank Mining: A General Hard Example Mining Method for Real-time DetectorsDetNet: A Backbone network for Object DetectionRobust Physical Adversarial Attack on Faster R-CNN Object DetectorAdvDetPatch: Attacking Object Detectors with Adversarial PatchesAttacking Object Detectors via Imperceptible Patches on BackgroundPhysical Adversarial Examples for Object DetectorsQuantization Mimic: Towards Very Tiny CNN for Object DetectionObject detection at 200 Frames Per SecondObject Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic ImagesSNIPER: Efficient Multi-Scale TrainingSoft Sampling for Robust Object DetectionMetaAnchor: Learning to Detect Objects with Customized AnchorsLocalization Recall Precision (LRP): A New Performance Metric for Object DetectionAuto-Context R-CNNPooling Pyramid Network for Object DetectionModeling Visual Context is Key to Augmenting Object Detection DatasetsDual Refinement Network for Single-Shot Object DetectionAcquisition of Localization Confidence for Accurate Object DetectionCornerNet: Detecting Objects as Paired KeypointsUnsupervised Hard Example Mining from Videos for Improved Object DetectionSAN: Learning Relationship between Convolutional Features for Multi-Scale Object DetectionA Survey of Modern Object Detection Literature using Deep LearningTiny-DSOD: Lightweight Object Detection for Resource-Restricted UsagesDeep Feature Pyramid Reconfiguration for Object DetectionMDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object DetectionRecent Advances in Object Detection in the Age of Deep Convolutional Neural NetworksDeep Learning for Generic Object Detection: A SurveyTraining Confidence-Calibrated Classifier for Detecting Out-of-Distribution SamplesScratchDet:Exploring to Train Single-Shot Object Detectors from ScratchFast and accurate object detection in high resolution 4K and 8K video using GPUsHybrid Knowledge Routed Modules for Large-scale Object DetectionGradient Harmonized Single-stage DetectorM2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid NetworkBAN: Focusing on Boundary Context for Object DetectionMulti-layer Pruning Framework for Compressing Single Shot MultiBox DetectorR2CNN++: Multi-Dimensional Attention Based Rotation Invariant Detector with Robust Anchor StrategyDeRPN: Taking a further step toward more general object detectionFast Efficient Object Detection Using Selective AttentionSampling Techniques for Large-Scale Object Detection from Sparsely Annotated ObjectsEfficient Coarse-to-Fine Non-Local Module for the Detection of Small ObjectsDeep Regionlets: Blended Representation and Deep Learning for Generic Object DetectionGrid R-CNNTransferable Adversarial Attacks for Image and Video Object DetectionAnchor Box Optimization for Object DetectionAutoFocus: Efficient Multi-Scale InferencePractical Adversarial Attack Against Object DetectorLearning Efficient Detector with Semi-supervised Adaptive DistillationScale-Aware Trident Networks for Object DetectionRegion Proposal by Guided AnchoringConsistent Optimization for Single-Shot Object DetectionBottom-up Object Detection by Grouping Extreme and Center PointsA Single-shot Object Detector with Feature Aggragation and EnhancementBag of Freebies for Training Object Detection Neural NetworksNon-Maximum Suppression (NMS)End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum SuppressionA convnet for non-maximum suppressionSoft-NMS – Improving Object Detection With One Line of CodeLearning non-maximum suppressionRelation Networks for Object DetectionLearning Pairwise Relationship for Multi-object Detection in Crowded ScenesDaedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial ExamplesAdversarial ExamplesAdversarial Examples that Fool DetectorsAdversarial Examples Are Not Easily Detected: Bypassing Ten Detection MethodsWeakly Supervised Object DetectionTrack and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object DetectionWeakly supervised object detection using pseudo-strong labelsSaliency Guided End-to-End Learning for Weakly Supervised Object DetectionVisual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object DetectionVideo Object DetectionLearning Object Class Detectors from Weakly Annotated VideoAnalysing domain shift factors between videos and images for object detectionVideo Object RecognitionDeep Learning for Saliency Prediction in Natural VideoT-CNN: Tubelets with Convolutional Neural Networks for Object Detection from VideosObject Detection from Video Tubelets with Convolutional Neural NetworksObject Detection in Videos with Tubelets and Multi-context CuesContext Matters: Refining Object Detection in Video with Recurrent Neural NetworksCNN Based Object Detection in Large Video ImagesObject Detection in Videos with Tubelet Proposal NetworksFlow-Guided Feature Aggregation for Video Object DetectionVideo Object Detection using Faster R-CNNImproving Context Modeling for Video Object Detection and TrackingTemporal Dynamic Graph LSTM for Action-driven Video Object DetectionMobile Video Object Detection with Temporally-Aware Feature MapsTowards High Performance Video Object DetectionImpression Network for Video Object DetectionSpatial-Temporal Memory Networks for Video Object Detection3D-DETNet: a Single Stage Video-Based Vehicle DetectorObject Detection in Videos by Short and Long Range Object LinkingObject Detection in Video with Spatiotemporal Sampling NetworksTowards High Performance Video Object Detection for MobilesOptimizing Video Object Detection via a Scale-Time LatticePack and Detect: Fast Object Detection in Videos Using Region-of-Interest PackingFast Object Detection in Compressed VideoTube-CNN: Modeling temporal evolution of appearance for object detection in videoAdaScale: Towards Real-time Video Object Detection Using Adaptive ScalingObject Detection on Mobile DevicesPelee: A Real-Time Object Detection System on Mobile DevicesObject Detection in 3DVote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural NetworksComplex-YOLO: Real-time 3D Object Detection on Point CloudsFocal Loss in 3D Object Detection3D Object Detection Using Scale Invariant and Feature Reweighting Networks3D Backbone Network for 3D Object DetectionObject Detection on RGB-DLearning Rich Features from RGB-D Images for Object Detection and SegmentationDifferential Geometry Boosts Convolutional Neural Networks for Object DetectionA Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose EstimationZero-Shot Object DetectionZero-Shot DetectionZero-Shot Object DetectionZero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel ConceptsZero-Shot Object Detection by Hybrid Region EmbeddingSalient Object DetectionBest Deep Saliency Detection Models (CVPR 2016 & 2015)Large-scale optimization of hierarchical features for saliency prediction in natural imagesPredicting Eye Fixations using Convolutional Neural NetworksSaliency Detection by Multi-Context Deep LearningDeepSaliency: Multi-Task Deep Neural Network Model for Salient Object DetectionSuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object DetectionShallow and Deep Convolutional Networks for Saliency PredictionRecurrent Attentional Networks for Saliency DetectionTwo-Stream Convolutional Networks for Dynamic Saliency PredictionUnconstrained Salient Object DetectionUnconstrained Salient Object Detection via Proposal Subset OptimizationDHSNet: Deep Hierarchical Saliency Network for Salient Object DetectionSalient Object SubitizingDeeply-Supervised Recurrent Convolutional Neural Network for Saliency DetectionSaliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNsEdge Preserving and Multi-Scale Contextual Neural Network for Salient Object DetectionA Deep Multi-Level Network for Saliency PredictionVisual Saliency Detection Based on Multiscale Deep CNN FeaturesA Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency DetectionDeeply supervised salient object detection with short connectionsWeakly Supervised Top-down Salient Object DetectionSalGAN: Visual Saliency Prediction with Generative Adversarial NetworksVisual Saliency Prediction Using a Mixture of Deep Neural NetworksA Fast and Compact Salient Score Regression Network Based on Fully Convolutional NetworkSaliency Detection by Forward and Backward Cues in Deep-CNNsSupervised Adversarial Networks for Image Saliency DetectionGroup-wise Deep Co-saliency DetectionTowards the Success Rate of One: Real-time Unconstrained Salient Object DetectionAmulet: Aggregating Multi-level Convolutional Features for Salient Object DetectionLearning Uncertain Convolutional Features for Accurate Saliency DetectionDeep Edge-Aware Saliency DetectionSelf-explanatory Deep Salient Object DetectionPiCANet: Learning Pixel-wise Contextual Attention in ConvNets and Its Application in Saliency DetectionDeepFeat: A Bottom Up and Top Down Saliency Model Based on Deep Features of Convolutional Neural NetsRecurrently Aggregating Deep Features for Salient Object DetectionDeep saliency: What is learnt by a deep network about saliency?Contrast-Oriented Deep Neural Networks for Salient Object DetectionSalient Object Detection by Lossless Feature ReflectionHyperFusion-Net: Densely Reflective Fusion for Salient Object DetectionVideo Saliency DetectionDeep Learning For Video Saliency DetectionVideo Salient Object Detection Using Spatiotemporal Deep FeaturesPredicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTMVisual Relationship DetectionVisual Relationship Detection with Language PriorsViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship DetectionVisual Translation Embedding Network for Visual Relation DetectionDeep Variation-structured Reinforcement Learning for Visual Relationship and Attribute DetectionDetecting Visual Relationships with Deep Relational NetworksIdentifying Spatial Relations in Images using Convolutional Neural NetworksPPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCNNatural Language Guided Visual Relationship DetectionDetecting Visual Relationships Using Box AttentionGoogle AI Open Images - Visual Relationship TrackContext-Dependent Diffusion Network for Visual Relationship DetectionA Problem Reduction Approach for Visual Relationships DetectionFace DetecitonMulti-view Face Detection Using Deep Convolutional Neural NetworksFrom Facial Parts Responses to Face Detection: A Deep Learning ApproachCompact Convolutional Neural Network Cascade for Face DetectionFace Detection with End-to-End Integration of a ConvNet and a 3D ModelCMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face DetectionTowards a Deep Learning Framework for Unconstrained Face DetectionSupervised Transformer Network for Efficient Face DetectionUnitBox: An Advanced Object Detection NetworkBootstrapping Face Detection with Hard Negative ExamplesGrid Loss: Detecting Occluded FacesA Multi-Scale Cascade Fully Convolutional Network Face DetectorMTCNNJoint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural NetworksFace Detection using Deep Learning: An Improved Faster RCNN ApproachFaceness-Net: Face Detection through Deep Facial Part ResponsesMulti-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained “Hard Faces”End-To-End Face Detection and RecognitionFace R-CNNFace Detection through Scale-Friendly Deep Convolutional NetworksScale-Aware Face DetectionDetecting Faces Using Inside Cascaded Contextual CNNMulti-Branch Fully Convolutional Network for Face DetectionSSH: Single Stage Headless Face DetectorDockerface: an easy to install and use Faster R-CNN face detector in a Docker containerFaceBoxes: A CPU Real-time Face Detector with High AccuracyS3FD: Single Shot Scale-invariant Face DetectorDetecting Faces Using Region-based Fully Convolutional NetworksAffordanceNet: An End-to-End Deep Learning Approach for Object Affordance DetectionFace Attention Network: An effective Face Detector for the Occluded FacesFeature Agglomeration Networks for Single Stage Face DetectionFace Detection Using Improved Faster RCNNPyramidBox: A Context-assisted Single Shot Face DetectorA Fast Face Detection Method via Convolutional Neural NetworkBeyond Trade-off: Accelerate FCN-based Face Detector with Higher AccuracyReal-Time Rotation-Invariant Face Detection with Progressive Calibration NetworksSFace: An Efficient Network for Face Detection in Large Scale VariationsSurvey of Face Detection on Low-quality ImagesAnchor Cascade for Efficient Face DetectionAdversarial Attacks on Face Detectors using Neural Net based Constrained OptimizationSelective Refinement Network for High Performance Face DetectionDSFD: Dual Shot Face DetectorLearning Better Features for Face Detection with Feature Fusion and Segmentation SupervisionFA-RPN: Floating Region Proposals for Face DetectionRobust and High Performance Face DetectorDAFE-FD: Density Aware Feature Enrichment for Face DetectionImproved Selective Refinement Network for Face DetectionRevisiting a single-stage method for face detectionDetect Small FacesFinding Tiny FacesDetecting and counting tiny facesSeeing Small Faces from Robust Anchor’s PerspectiveFace-MagNet: Magnifying Feature Maps to Detect Small FacesRobust Face Detection via Learning Small Faces on Hard ImagesSFA: Small Faces Attention Face DetectorPerson Head DetectionContext-aware CNNs for person head detectionDetecting Heads using Feature Refine Net and Cascaded Multi-scale ArchitectureA Comparison of CNN-based Face and Head Detectors for Real-Time Video Surveillance ApplicationsFCHD: A fast and accurate head detectorPedestrian Detection / People DetectionPedestrian Detection aided by Deep Learning Semantic TasksDeep Learning Strong Parts for Pedestrian DetectionTaking a Deeper Look at PedestriansConvolutional Channel FeaturesEnd-to-end people detection in crowded scenesLearning Complexity-Aware Cascades for Deep Pedestrian DetectionDeep convolutional neural networks for pedestrian detectionScale-aware Fast R-CNN for Pedestrian DetectionNew algorithm improves speed and accuracy of pedestrian detectionPushing the Limits of Deep CNNs for Pedestrian DetectionA Real-Time Deep Learning Pedestrian Detector for Robot NavigationA Real-Time Pedestrian Detector using Deep Learning for Human-Aware NavigationIs Faster R-CNN Doing Well for Pedestrian Detection?Unsupervised Deep Domain Adaptation for Pedestrian DetectionReduced Memory Region Based Deep Convolutional Neural Network DetectionFused DNN: A deep neural network fusion approach to fast and robust pedestrian detectionDetecting People in Artwork with CNNsMultispectral Deep Neural Networks for Pedestrian DetectionBox-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian DetectionDeep Multi-camera People DetectionExpecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial ImpostersWhat Can Help Pedestrian Detection?Illuminating Pedestrians via Simultaneous Detection & SegmentationRotational Rectification Network for Robust Pedestrian DetectionSTD-PD: Generating Synthetic Training Data for Pedestrian Detection in Unannotated VideosToo Far to See? Not Really! — Pedestrian Detection with Scale-aware Localization PolicyRepulsion Loss: Detecting Pedestrians in a CrowdAggregated Channels Network for Real-Time Pedestrian DetectionIllumination-aware Faster R-CNN for Robust Multispectral Pedestrian DetectionExploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian DetectionPedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and BeyondPCN: Part and Context Information for Pedestrian Detection with CNNsSmall-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature AggregationOcclusion-aware R-CNN: Detecting Pedestrians in a CrowdMultispectral Pedestrian Detection via Simultaneous Detection and SegmentationPedestrian Detection with Autoregressive Network PhasesThe Cross-Modality Disparity Problem in Multispectral Pedestrian DetectionVehicle DetectionDAVE: A Unified Framework for Fast Vehicle Detection and AnnotationEvolving Boxes for fast Vehicle DetectionFine-Grained Car Detection for Visual Census EstimationSINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle DetectionLabel and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled DataDomain Randomization for Scene-Specific Car Detection and Pose EstimationShuffleDet: Real-Time Vehicle Detection Network in On-board Embedded UAV ImageryTraffic-Sign DetectionTraffic-Sign Detection and Classification in the WildEvaluating State-of-the-art Object Detector on Challenging Traffic Light DataDetecting Small Signs from Large ImagesLocalized Traffic Sign Detection with Multi-scale Deconvolution NetworksDetecting Traffic Lights by Single Shot DetectionA Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light DetectionSkeleton DetectionObject Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side OutputsDeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural ImagesSRN: Side-output Residual Network for Object Symmetry Detection in the WildHi-Fi: Hierarchical Feature Integration for Skeleton DetectionFruit DetectionDeep Fruit Detection in OrchardsImage Segmentation for Fruit Detection and Yield Estimation in Apple OrchardsShadow DetectionFast Shadow Detection from a Single Image Using a Patched Convolutional Neural NetworkA+D-Net: Shadow Detection with Adversarial Shadow AttenuationStacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow RemovalDirection-aware Spatial Context Features for Shadow DetectionDirection-aware Spatial Context Features for Shadow Detection and RemovalOthers DetectionDeep Deformation Network for Object Landmark LocalizationFashion Landmark Detection in the WildDeep Learning for Fast and Accurate Fashion Item DetectionOSMDeepOD - OSM and Deep Learning based Object Detection from Aerial Imagery (formerly known as “OSM-Crosswalk-Detection”)Selfie Detection by Synergy-Constraint Based Convolutional Neural NetworkAssociative Embedding:End-to-End Learning for Joint Detection and GroupingDeep Cuboid Detection: Beyond 2D Bounding BoxesAutomatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds DetectionDeep Learning Logo Detection with Data Expansion by Synthesising ContextScalable Deep Learning Logo DetectionPixel-wise Ear Detection with Convolutional Encoder-Decoder NetworksAutomatic Handgun Detection Alarm in Videos Using Deep LearningObjects as context for part detectionUsing Deep Networks for Drone DetectionCut, Paste and Learn: Surprisingly Easy Synthesis for Instance DetectionTarget Driven Instance DetectionDeepVoting: An Explainable Framework for Semantic Part Detection under Partial OcclusionVPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and RecognitionGrab, Pay and Eat: Semantic Food Detection for Smart RestaurantsReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance VideosDeep Learning Object Detection Methods for Ecological Camera Trap DataEL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane DetectionTowards End-to-End Lane Detection: an Instance Segmentation ApproachiCAN: Instance-Centric Attention Network for Human-Object Interaction DetectionDensely Supervised Grasp Detector (DSGD)Object ProposalDeepProposal: Hunting Objects by Cascading Deep Convolutional LayersScale-aware Pixel-wise Object Proposal NetworksAttend Refine Repeat: Active Box Proposal Generation via In-Out LocalizationLearning to Segment Object Proposals via Recursive Neural NetworksLearning Detection with Diverse ProposalsScaleNet: Guiding Object Proposal Generation in Supermarkets and BeyondImproving Small Object Proposals for Company Logo DetectionOpen Logo Detection ChallengeAttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small ObjectsLocalizationBeyond Bounding Boxes: Precise Localization of Objects in ImagesWeakly Supervised Object Localization with Multi-fold Multiple Instance LearningWeakly Supervised Object Localization Using Size EstimatesActive Object Localization with Deep Reinforcement LearningLocalizing objects using referring expressionsLocNet: Improving Localization Accuracy for Object DetectionLearning Deep Features for Discriminative LocalizationContextLocNet: Context-Aware Deep Network Models for Weakly Supervised LocalizationEnsemble of Part Detectors for Simultaneous Classification and LocalizationSTNet: Selective Tuning of Convolutional Networks for Object LocalizationSoft Proposal Networks for Weakly Supervised Object LocalizationFine-grained Discriminative Localization via Saliency-guided Faster R-CNNTutorials / TalksConvolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detectionTowards Good Practices for Recognition & DetectionWork in progress: Improving object detection and instance segmentation for small objectsObject Detection with Deep Learning: A ReviewProjectsDetectronTensorBox: a simple framework for training neural networks to detect objects in imagesObject detection in torch: Implementation of some object detection frameworks in torchUsing DIGITS to train an Object Detection networkFCN-MultiBox DetectorKittiBox: A car detection model implemented in Tensorflow.Deformable Convolutional Networks + MST + Soft-NMSHow to Build a Real-time Hand-Detector using Neural Networks (SSD) on TensorflowMetrics for object detectionMobileNetv2-SSDLiteLeaderboardDetection Results: VOC2012ToolsBeaverDam: Video annotation tool for deep learning training labelsBlogsConvolutional Neural Networks for Object DetectionIntroducing automatic object detection to visual search (Pinterest)Deep Learning for Object Detection with DIGITSAnalyzing The Papers Behind Facebook’s Computer Vision ApproachEasily Create High Quality Object Detectors with Deep LearningHow to Train a Deep-Learned Object Detection Model in the Microsoft Cognitive ToolkitObject Detection in Satellite Imagery, a Low Overhead ApproachYou Only Look Twice?—?Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural NetworksFaster R-CNN Pedestrian and Car DetectionSmall U-Net for vehicle detectionRegion of interest pooling explainedSupercharge your Computer Vision models with the TensorFlow Object Detection APIUnderstanding SSD MultiBox?—?Real-Time Object Detection In Deep LearningOne-shot object detectionAn overview of object detection: one-stage methodsdeep learning object detection
| Method | backbone | test size | VOC2007 | VOC2010 | VOC2012 | ILSVRC 2013 | MSCOCO 2015 | Speed |
|---|---|---|---|---|---|---|---|---|
| OverFeat | 24.3% | |||||||
| R-CNN | AlexNet | 58.5% | 53.7% | 53.3% | 31.4% | |||
| R-CNN | VGG17 | 66.0% | ||||||
| SPP_net | ZF-5 | 54.2% | 31.84% | |||||
| DeepID-Net | 64.1% | 50.3% | ||||||
| NoC | 73.3% | 68.8% | ||||||
| Fast-RCNN | VGG16 | 70.0% | 68.8% | 68.4% | 19.7%(@[0.5-0.95]), 35.9%(@0.5) | |||
| MR-CNN | 78.2% | 73.9% | ||||||
| Faster-RCNN | VGG16 | 78.8% | 75.9% | 21.9%(@[0.5-0.95]), 42.7%(@0.5) | 198ms | |||
| Faster-RCNN | ResNet101 | 85.6% | 83.8% | 37.4%(@[0.5-0.95]), 59.0%(@0.5) | ||||
| YOLO | 63.4% | 57.9% | 45 fps | |||||
| YOLO | VGG-16 | 66.4% | 21 fps | |||||
| YOLOv2 | 448x448 | 78.6% | 73.4% | 21.6%(@[0.5-0.95]), 44.0%(@0.5) | 40 fps | |||
| SSD | VGG16 | 300x300 | 77.2% | 75.8% | 25.1%(@[0.5-0.95]), 43.1%(@0.5) | 46 fps | ||
| SSD | VGG16 | 512x512 | 79.8% | 78.5% | 28.8%(@[0.5-0.95]), 48.5%(@0.5) | 19 fps | ||
| SSD | ResNet101 | 300x300 | 28.0%(@[0.5-0.95]) | 16 fps | ||||
| SSD | ResNet101 | 512x512 | 31.2%(@[0.5-0.95]) | 8 fps | ||||
| DSSD | ResNet101 | 300x300 | 28.0%(@[0.5-0.95]) | 8 fps | ||||
| DSSD | ResNet101 | 500x500 | 33.2%(@[0.5-0.95]) | 6 fps | ||||
| ION | 79.2% | 76.4% | ||||||
| CRAFT | 75.7% | 71.3% | 48.5% | |||||
| OHEM | 78.9% | 76.3% | 25.5%(@[0.5-0.95]), 45.9%(@0.5) | |||||
| R-FCN | ResNet50 | 77.4% | 0.12sec(K40), 0.09sec(TitianX) | |||||
| R-FCN | ResNet101 | 79.5% | 0.17sec(K40), 0.12sec(TitianX) | |||||
| R-FCN(ms train) | ResNet101 | 83.6% | 82.0% | 31.5%(@[0.5-0.95]), 53.2%(@0.5) | ||||
| PVANet 9.0 | 84.9% | 84.2% | 750ms(CPU), 46ms(TitianX) | |||||
| RetinaNet | ResNet101-FPN | |||||||
| Light-Head R-CNN | Xception* | 800/1200 | 31.5%@[0.5:0.95] | 95 fps | ||||
| Light-Head R-CNN | Xception* | 700/1100 | 30.7%@[0.5:0.95] | 102 fps | ||||
| STDN | 80.9 (07+12) | |||||||
| RefineDet | 83.8 (07+12) | 83.5 (07++12) | 41.8 | |||||
| SNIP | 45.7 | |||||||
| Relation-Network | 32.5 | |||||||
| Cascade R-CNN | 42.8 | |||||||
| MLKP | 80.6 (07+12) | 77.2 (07++12) | 28.6 | |||||
| Fitness-NMS | 41.8 | |||||||
| RFBNet | 82.2 (07+12) | |||||||
| CornerNet | 42.1 | |||||||
| PFPNet | 84.1 (07+12) | 83.7 (07++12) | 39.4 | |||||
| Pelee | 70.9 (07+12) | |||||||
| HKRM | 78.8 (07+12) | 37.8 | ||||||
| M2Det | 44.2 | |||||||
| SIN | 76.0 (07+12) | 73.1 (07++12) | 23.2 |
Papers
損失函數(shù)
[CVPR2019] Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
arxiv: https://arxiv.org/abs/1902.09630
Deep Neural Networks for Object Detection
paper: http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
arxiv: http://arxiv.org/abs/1312.6229
github: https://github.com/sermanet/OverFeat
code: http://cilvr.nyu.edu/doku.php?id=software:overfeat:start
R-CNN
Rich feature hierarchies for accurate object detection and semantic segmentation
intro: R-CNN
arxiv: http://arxiv.org/abs/1311.2524
supp: http://people.eecs.berkeley.edu/~rbg/papers/r-cnn-cvpr-supp.pdf
slides: http://www.image-net.org/challenges/LSVRC/2013/slides/r-cnn-ilsvrc2013-workshop.pdf
slides: http://www.cs.berkeley.edu/~rbg/slides/rcnn-cvpr14-slides.pdf
github: https://github.com/rbgirshick/rcnn
notes: http://zhangliliang.com/2014/07/23/paper-note-rcnn/
caffe-pr(“Make R-CNN the Caffe detection example”): https://github.com/BVLC/caffe/pull/482
Fast R-CNN
Fast R-CNN
arxiv: http://arxiv.org/abs/1504.08083
slides: http://tutorial.caffe.berkeleyvision.org/caffe-cvpr15-detection.pdf
github: https://github.com/rbgirshick/fast-rcnn
github(COCO-branch): https://github.com/rbgirshick/fast-rcnn/tree/coco
webcam demo: https://github.com/rbgirshick/fast-rcnn/pull/29
notes: http://zhangliliang.com/2015/05/17/paper-note-fast-rcnn/
notes: http://blog.csdn.net/linj_m/article/details/48930179
github(“Fast R-CNN in MXNet”): https://github.com/precedenceguo/mx-rcnn
github: https://github.com/mahyarnajibi/fast-rcnn-torch
github: https://github.com/apple2373/chainer-simple-fast-rnn
github: https://github.com/zplizzi/tensorflow-fast-rcnn
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
intro: CVPR 2017
arxiv: https://arxiv.org/abs/1704.03414
paper: http://abhinavsh.info/papers/pdfs/adversarial_object_detection.pdf
github(Caffe): https://github.com/xiaolonw/adversarial-frcnn
Faster R-CNN
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
intro: NIPS 2015
arxiv: http://arxiv.org/abs/1506.01497
gitxiv: http://www.gitxiv.com/posts/8pfpcvefDYn2gSgXk/faster-r-cnn-towards-real-time-object-detection-with-region
slides: http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w05-FasterR-CNN.pdf
github(official, Matlab): https://github.com/ShaoqingRen/faster_rcnn
github: https://github.com/rbgirshick/py-faster-rcnn
github(MXNet): https://github.com/msracver/Deformable-ConvNets/tree/master/faster_rcnn
github: https://github.com//jwyang/faster-rcnn.pytorch
github: https://github.com/mitmul/chainer-faster-rcnn
github: https://github.com/andreaskoepf/faster-rcnn.torch
github: https://github.com/ruotianluo/Faster-RCNN-Densecap-torch
github: https://github.com/smallcorgi/Faster-RCNN_TF
github: https://github.com/CharlesShang/TFFRCNN
github(C++ demo): https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus
github: https://github.com/yhenon/keras-frcnn
github: https://github.com/Eniac-Xie/faster-rcnn-resnet
github(C++): https://github.com/D-X-Y/caffe-faster-rcnn/tree/dev
R-CNN minus R
intro: BMVC 2015
arxiv: http://arxiv.org/abs/1506.06981
Faster R-CNN in MXNet with distributed implementation and data parallelization
github: https://github.com/dmlc/mxnet/tree/master/example/rcnn
Contextual Priming and Feedback for Faster R-CNN
intro: ECCV 2016. Carnegie Mellon University
paper: http://abhinavsh.info/context_priming_feedback.pdf
poster: http://www.eccv2016.org/files/posters/P-1A-20.pdf
An Implementation of Faster RCNN with Study for Region Sampling
intro: Technical Report, 3 pages. CMU
arxiv: https://arxiv.org/abs/1702.02138
github: https://github.com/endernewton/tf-faster-rcnn
Interpretable R-CNN
intro: North Carolina State University & Alibaba
keywords: AND-OR Graph (AOG)
arxiv: https://arxiv.org/abs/1711.05226
[AAAI2019]Object Detection based on Region Decomposition and Assembly
intro: AAAI2019,區(qū)域分解組裝
arxiv: https://arxiv.org/abs/1901.08225
translate: https://zhuanlan.zhihu.com/p/58951221 論文翻譯
Light-Head R-CNN
Light-Head R-CNN: In Defense of Two-Stage Object Detector
intro: Tsinghua University & Megvii Inc
arxiv: https://arxiv.org/abs/1711.07264
github(official, Tensorflow): https://github.com/zengarden/light_head_rcnn
github: https://github.com/terrychenism/Deformable-ConvNets/blob/master/rfcn/symbols/resnet_v1_101_rfcn_light.py
##Cascade R-CNN
Cascade R-CNN: Delving into High Quality Object Detection
intro: CVPR 2018. UC San Diego
arxiv: https://arxiv.org/abs/1712.00726
github(Caffe, official): https://github.com/zhaoweicai/cascade-rcnn
MultiBox
Scalable Object Detection using Deep Neural Networks
intro: first MultiBox. Train a CNN to predict Region of Interest.
arxiv: http://arxiv.org/abs/1312.2249
github: https://github.com/google/multibox
blog: https://research.googleblog.com/2014/12/high-quality-object-detection-at-scale.html
Scalable, High-Quality Object Detection
intro: second MultiBox
arxiv: http://arxiv.org/abs/1412.1441
github: https://github.com/google/multibox
SPP-Net
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
intro: ECCV 2014 / TPAMI 2015
arxiv: http://arxiv.org/abs/1406.4729
github: https://github.com/ShaoqingRen/SPP_net
notes: http://zhangliliang.com/2014/09/13/paper-note-sppnet/
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
intro: PAMI 2016
intro: an extension of R-CNN. box pre-training, cascade on region proposals, deformation layers and context representations
project page: http://www.ee.cuhk.edu.hk/?wlouyang/projects/imagenetDeepId/index.html
arxiv: http://arxiv.org/abs/1412.5661
Object Detectors Emerge in Deep Scene CNNs
intro: ICLR 2015
arxiv: http://arxiv.org/abs/1412.6856
paper: https://www.robots.ox.ac.uk/~vgg/rg/papers/zhou_iclr15.pdf
paper: https://people.csail.mit.edu/khosla/papers/iclr2015_zhou.pdf
slides: http://places.csail.mit.edu/slide_iclr2015.pdf
segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
intro: CVPR 2015
project(code+data): https://www.cs.toronto.edu/~yukun/segdeepm.html
arxiv: https://arxiv.org/abs/1502.04275
github: https://github.com/YknZhu/segDeepM
Object Detection Networks on Convolutional Feature Maps
intro: TPAMI 2015
keywords: NoC
arxiv: http://arxiv.org/abs/1504.06066
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
arxiv: http://arxiv.org/abs/1504.03293
slides: http://www.ytzhang.net/files/publications/2015-cvpr-det-slides.pdf
github: https://github.com/YutingZhang/fgs-obj
DeepBox: Learning Objectness with Convolutional Networks
keywords: DeepBox
arxiv: http://arxiv.org/abs/1505.02146
github: https://github.com/weichengkuo/DeepBox
MR-CNN
Object detection via a multi-region & semantic segmentation-aware CNN model
intro: ICCV 2015. MR-CNN
arxiv: http://arxiv.org/abs/1505.01749
github: https://github.com/gidariss/mrcnn-object-detection
notes: http://zhangliliang.com/2015/05/17/paper-note-ms-cnn/
notes: http://blog.cvmarcher.com/posts/2015/05/17/multi-region-semantic-segmentation-aware-cnn/
YOLO
You Only Look Once: Unified, Real-Time Object Detection
arxiv: http://arxiv.org/abs/1506.02640
code: http://pjreddie.com/darknet/yolo/
github: https://github.com/pjreddie/darknet
blog: https://pjreddie.com/publications/yolo/
slides: https://docs.google.com/presentation/d/1aeRvtKG21KHdD5lg6Hgyhx5rPq_ZOsGjG5rJ1HP7BbA/pub?start=false&loop=false&delayms=3000&slide=id.p
reddit: https://www.reddit.com/r/MachineLearning/comments/3a3m0o/realtime_object_detection_with_yolo/
github: https://github.com/gliese581gg/YOLO_tensorflow
github: https://github.com/xingwangsfu/caffe-yolo
github: https://github.com/frankzhangrui/Darknet-Yolo
github: https://github.com/BriSkyHekun/py-darknet-yolo
github: https://github.com/tommy-qichang/yolo.torch
github: https://github.com/frischzenger/yolo-windows
github: https://github.com/AlexeyAB/yolo-windows
github: https://github.com/nilboy/tensorflow-yolo
darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++
blog: https://thtrieu.github.io/notes/yolo-tensorflow-graph-buffer-cpp
github: https://github.com/thtrieu/darkflow
Start Training YOLO with Our Own Data
intro: train with customized data and class numbers/labels. Linux / Windows version for darknet.
blog: http://guanghan.info/blog/en/my-works/train-yolo/
github: https://github.com/Guanghan/darknet
YOLO: Core ML versus MPSNNGraph
intro: Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API.
blog: http://machinethink.net/blog/yolo-coreml-versus-mps-graph/
github: https://github.com/hollance/YOLO-CoreML-MPSNNGraph
TensorFlow YOLO object detection on Android
intro: Real-time object detection on Android using the YOLO network with TensorFlow
github: https://github.com/natanielruiz/android-yolo
Computer Vision in iOS – Object Detection
blog: https://sriraghu.com/2017/07/12/computer-vision-in-ios-object-detection/
github:https://github.com/r4ghu/iOS-CoreML-Yolo
YOLOv2
YOLO9000: Better, Faster, Stronger
arxiv: https://arxiv.org/abs/1612.08242
code: http://pjreddie.com/yolo9000/
github(Chainer): https://github.com/leetenki/YOLOv2
github(Keras): https://github.com/allanzelener/YAD2K
github(PyTorch): https://github.com/longcw/yolo2-pytorch
github(Tensorflow): https://github.com/hizhangp/yolo_tensorflow
github(Windows): https://github.com/AlexeyAB/darknet
github: https://github.com/choasUp/caffe-yolo9000
github: https://github.com/philipperemy/yolo-9000
darknet_scripts
intro: Auxilary scripts to work with (YOLO) darknet deep learning famework. AKA -> How to generate YOLO anchors?
github: https://github.com/Jumabek/darknet_scripts
Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2
github: https://github.com/AlexeyAB/Yolo_mark
LightNet: Bringing pjreddie’s DarkNet out of the shadows
github: https://github.com//explosion/lightnet
YOLO v2 Bounding Box Tool
intro: Bounding box labeler tool to generate the training data in the format YOLO v2 requires.
github: https://github.com/Cartucho/yolo-boundingbox-labeler-GUI
YOLOv3
YOLOv3: An Incremental Improvement
project page: https://pjreddie.com/darknet/yolo/
arxiv: https://arxiv.org/abs/1804.02767
github: https://github.com/DeNA/PyTorch_YOLOv3
github: https://github.com/eriklindernoren/PyTorch-YOLOv3
YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers
arxiv：https://arxiv.org/abs/1811.05588
AttentionNet: Aggregating Weak Directions for Accurate Object Detection
intro: ICCV 2015
intro: state-of-the-art performance of 65% (AP) on PASCAL VOC 2007/2012 human detection task
arxiv: http://arxiv.org/abs/1506.07704
slides: https://www.robots.ox.ac.uk/~vgg/rg/slides/AttentionNet.pdf
slides: http://image-net.org/challenges/talks/lunit-kaist-slide.pdf
DenseBox
DenseBox: Unifying Landmark Localization with End to End Object Detection
arxiv: http://arxiv.org/abs/1509.04874
demo: http://pan.baidu.com/s/1mgoWWsS
KITTI result: http://www.cvlibs.net/datasets/kitti/eval_object.php
SSD
SSD: Single Shot MultiBox Detector
intro: ECCV 2016 Oral
arxiv: http://arxiv.org/abs/1512.02325
paper: http://www.cs.unc.edu/~wliu/papers/ssd.pdf
slides: http://www.cs.unc.edu/~wliu/papers/ssd_eccv2016_slide.pdf
github(Official): https://github.com/weiliu89/caffe/tree/ssd
video: http://weibo.com/p/2304447a2326da963254c963c97fb05dd3a973
github: https://github.com/zhreshold/mxnet-ssd
github: https://github.com/zhreshold/mxnet-ssd.cpp
github: https://github.com/rykov8/ssd_keras
github: https://github.com/balancap/SSD-Tensorflow
github: https://github.com/amdegroot/ssd.pytorch
github(Caffe): https://github.com/chuanqi305/MobileNet-SSD
What’s the diffience in performance between this new code you pushed and the previous code? #327
https://github.com/weiliu89/caffe/issues/327
DSSD
DSSD : Deconvolutional Single Shot Detector
intro: UNC Chapel Hill & Amazon Inc
arxiv: https://arxiv.org/abs/1701.06659
github: https://github.com/chengyangfu/caffe/tree/dssd
github: https://github.com/MTCloudVision/mxnet-dssd
demo: http://120.52.72.53/www.cs.unc.edu/c3pr90ntc0td/~cyfu/dssd_lalaland.mp4
Enhancement of SSD by concatenating feature maps for object detection
intro: rainbow SSD (R-SSD)
arxiv: https://arxiv.org/abs/1705.09587
Context-aware Single-Shot Detector
keywords: CSSD, DiCSSD, DeCSSD, effective receptive fields (ERFs), theoretical receptive fields (TRFs)
arxiv: https://arxiv.org/abs/1707.08682
Feature-Fused SSD: Fast Detection for Small Objects
https://arxiv.org/abs/1709.05054
FSSD
FSSD: Feature Fusion Single Shot Multibox Detector
https://arxiv.org/abs/1712.00960
Weaving Multi-scale Context for Single Shot Detector
intro: WeaveNet
keywords: fuse multi-scale information
arxiv: https://arxiv.org/abs/1712.03149
ESSD
Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network
arxiv: https://arxiv.org/abs/1801.05918
Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection
arxiv: https://arxiv.org/abs/1802.06488
MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects
intro: Zhengzhou University
arxiv: https://arxiv.org/abs/1805.07009
Inside-Outside Net (ION)
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
intro: “0.8s per image on a Titan X GPU (excluding proposal generation) without two-stage bounding-box regression and 1.15s per image with it”.
arxiv: http://arxiv.org/abs/1512.04143
slides: http://www.seanbell.ca/tmp/ion-coco-talk-bell2015.pdf
coco-leaderboard: http://mscoco.org/dataset/
Adaptive Object Detection Using Adjacency and Zoom Prediction
intro: CVPR 2016. AZ-Net
arxiv: http://arxiv.org/abs/1512.07711
github: https://github.com/luyongxi/az-net
youtube: https://www.youtube.com/watch?v=YmFtuNwxaNM
G-CNN: an Iterative Grid Based Object Detector
arxiv: http://arxiv.org/abs/1512.07729
Factors in Finetuning Deep Model for object detection
Factors in Finetuning Deep Model for Object Detection with Long-tail Distribution
intro: CVPR 2016.rank 3rd for provided data and 2nd for external data on ILSVRC 2015 object detection
project page: http://www.ee.cuhk.edu.hk/~wlouyang/projects/ImageNetFactors/CVPR16.html
arxiv: http://arxiv.org/abs/1601.05150
We don’t need no bounding-boxes: Training object class detectors using only human verification
arxiv: http://arxiv.org/abs/1602.08405
HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
arxiv: http://arxiv.org/abs/1604.00600
A MultiPath Network for Object Detection
intro: BMVC 2016. Facebook AI Research (FAIR)
arxiv: http://arxiv.org/abs/1604.02135
github: https://github.com/facebookresearch/multipathnet
CRAFT
CRAFT Objects from Images
intro: CVPR 2016. Cascade Region-proposal-network And FasT-rcnn. an extension of Faster R-CNN
project page: http://byangderek.github.io/projects/craft.html
arxiv: https://arxiv.org/abs/1604.03239
paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Yang_CRAFT_Objects_From_CVPR_2016_paper.pdf
github: https://github.com/byangderek/CRAFT
OHEM
Training Region-based Object Detectors with Online Hard Example Mining
intro: CVPR 2016 Oral. Online hard example mining (OHEM)
arxiv: http://arxiv.org/abs/1604.03540
paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Shrivastava_Training_Region-Based_Object_CVPR_2016_paper.pdf
github(Official): https://github.com/abhi2610/ohem
author page: http://abhinav-shrivastava.info/
S-OHEM: Stratified Online Hard Example Mining for Object Detection
arxiv: https://arxiv.org/abs/1705.02233
Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers
intro: CVPR 2016
keywords: scale-dependent pooling (SDP), cascaded rejection classifiers (CRC)
paper: http://www-personal.umich.edu/~wgchoi/SDP-CRC_camready.pdf
R-FCN
R-FCN: Object Detection via Region-based Fully Convolutional Networks
arxiv: http://arxiv.org/abs/1605.06409
github: https://github.com/daijifeng001/R-FCN
github(MXNet): https://github.com/msracver/Deformable-ConvNets/tree/master/rfcn
github: https://github.com/Orpine/py-R-FCN
github: https://github.com/PureDiors/pytorch_RFCN
github: https://github.com/bharatsingh430/py-R-FCN-multiGPU
github: https://github.com/xdever/RFCN-tensorflow
R-FCN-3000 at 30fps: Decoupling Detection and Classification
arxiv: https://arxiv.org/abs/1712.01802
Recycle deep features for better object detection
arxiv: http://arxiv.org/abs/1607.05066
MS-CNN
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
intro: ECCV 2016
intro: 640×480: 15 fps, 960×720: 8 fps
arxiv: http://arxiv.org/abs/1607.07155
github: https://github.com/zhaoweicai/mscnn
poster: http://www.eccv2016.org/files/posters/P-2B-38.pdf
Multi-stage Object Detection with Group Recursive Learning
intro: VOC2007: 78.6%, VOC2012: 74.9%
arxiv: http://arxiv.org/abs/1608.05159
Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection
intro: WACV 2017. SubCNN
arxiv: http://arxiv.org/abs/1604.04693
github: https://github.com/tanshen/SubCNN
PVANET
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
intro: Presented at NIPS 2016 Workshop on Efficient Methods for Deep Neural Networks (EMDNN). Continuation of arXiv:1608.08021
arxiv: https://arxiv.org/abs/1611.08588
github: https://github.com/sanghoon/pva-faster-rcnn
leaderboard(PVANet 9.0): http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4
GBD-Net
Gated Bi-directional CNN for Object Detection
intro: The Chinese University of Hong Kong & Sensetime Group Limited
paper: http://link.springer.com/chapter/10.1007/978-3-319-46478-7_22
mirror: https://pan.baidu.com/s/1dFohO7v
Crafting GBD-Net for Object Detection
intro: winner of the ImageNet object detection challenge of 2016. CUImage and CUVideo
intro: gated bi-directional CNN (GBD-Net)
arxiv: https://arxiv.org/abs/1610.02579
github: https://github.com/craftGBD/craftGBD
StuffNet: Using ‘Stuff’ to Improve Object Detection
arxiv: https://arxiv.org/abs/1610.05861
Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene
arxiv: https://arxiv.org/abs/1610.09609
Hierarchical Object Detection with Deep Reinforcement Learning
intro: Deep Reinforcement Learning Workshop (NIPS 2016)
project page: https://imatge-upc.github.io/detection-2016-nipsws/
arxiv: https://arxiv.org/abs/1611.03718
slides: http://www.slideshare.net/xavigiro/hierarchical-object-detection-with-deep-reinforcement-learning
github: https://github.com/imatge-upc/detection-2016-nipsws
blog: http://jorditorres.org/nips/
Learning to detect and localize many objects from few examples
arxiv: https://arxiv.org/abs/1611.05664
Speed/accuracy trade-offs for modern convolutional object detectors
intro: CVPR 2017. Google Research
arxiv: https://arxiv.org/abs/1611.10012
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
arxiv: https://arxiv.org/abs/1612.01051
github: https://github.com/BichenWuUCB/squeezeDet
github: https://github.com/fregu856/2D_detection
Feature Pyramid Network (FPN)
Feature Pyramid Networks for Object Detection
intro: Facebook AI Research
arxiv: https://arxiv.org/abs/1612.03144
Action-Driven Object Detection with Top-Down Visual Attentions
arxiv: https://arxiv.org/abs/1612.06704
Beyond Skip Connections: Top-Down Modulation for Object Detection
intro: CMU & UC Berkeley & Google Research
arxiv: https://arxiv.org/abs/1612.06851
Wide-Residual-Inception Networks for Real-time Object Detection
intro: Inha University
arxiv: https://arxiv.org/abs/1702.01243
Attentional Network for Visual Object Detection
intro: University of Maryland & Mitsubishi Electric Research Laboratories
arxiv: https://arxiv.org/abs/1702.01478
Learning Chained Deep Features and Classifiers for Cascade in Object Detection
keykwords: CC-Net
intro: chained cascade network (CC-Net). 81.1% mAP on PASCAL VOC 2007
arxiv: https://arxiv.org/abs/1702.07054
DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling
intro: ICCV 2017 (poster)
arxiv: https://arxiv.org/abs/1703.10295
Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries
intro: CVPR 2017
arxiv: https://arxiv.org/abs/1704.03944
Spatial Memory for Context Reasoning in Object Detection
arxiv: https://arxiv.org/abs/1704.04224
Accurate Single Stage Detector Using Recurrent Rolling Convolution
intro: CVPR 2017. SenseTime
keywords: Recurrent Rolling Convolution (RRC)
arxiv: https://arxiv.org/abs/1704.05776
github: https://github.com/xiaohaoChen/rrc_detection
Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection
arxiv: https://arxiv.org/abs/1704.05775
LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems
intro: Embedded Vision Workshop in CVPR. UC San Diego & Qualcomm Inc
arxiv: https://arxiv.org/abs/1705.05922
Point Linking Network for Object Detection
intro: Point Linking Network (PLN)
arxiv: https://arxiv.org/abs/1706.03646
Perceptual Generative Adversarial Networks for Small Object Detection
arxiv: https://arxiv.org/abs/1706.05274
Few-shot Object Detection
arxiv: https://arxiv.org/abs/1706.08249
Yes-Net: An effective Detector Based on Global Information
arxiv: https://arxiv.org/abs/1706.09180
SMC Faster R-CNN: Toward a scene-specialized multi-object detector
arxiv: https://arxiv.org/abs/1706.10217
Towards lightweight convolutional neural networks for object detection
arxiv: https://arxiv.org/abs/1707.01395
RON: Reverse Connection with Objectness Prior Networks for Object Detection
intro: CVPR 2017
arxiv: https://arxiv.org/abs/1707.01691
github: https://github.com/taokong/RON
Mimicking Very Efficient Network for Object Detection
intro: CVPR 2017. SenseTime & Beihang University
paper: http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Mimicking_Very_Efficient_CVPR_2017_paper.pdf
Residual Features and Unified Prediction Network for Single Stage Detection
https://arxiv.org/abs/1707.05031
Deformable Part-based Fully Convolutional Network for Object Detection
intro: BMVC 2017 (oral). Sorbonne Universités & CEDRIC
arxiv: https://arxiv.org/abs/1707.06175
Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
intro: ICCV 2017
arxiv: https://arxiv.org/abs/1707.06399
Recurrent Scale Approximation for Object Detection in CNN
intro: ICCV 2017
keywords: Recurrent Scale Approximation (RSA)
arxiv: https://arxiv.org/abs/1707.09531
github: https://github.com/sciencefans/RSA-for-object-detection
DSOD
DSOD: Learning Deeply Supervised Object Detectors from Scratch
intro: ICCV 2017. Fudan University & Tsinghua University & Intel Labs China
arxiv: https://arxiv.org/abs/1708.01241
github: https://github.com/szq0214/DSOD
Object Detection from Scratch with Deep Supervision
arxiv: https://arxiv.org/abs/1809.09294
##RetinaNet
Focal Loss for Dense Object Detection
intro: ICCV 2017 Best student paper award. Facebook AI Research
keywords: RetinaNet
arxiv: https://arxiv.org/abs/1708.02002
Focal Loss Dense Detector for Vehicle Surveillance
arxiv: https://arxiv.org/abs/1803.01114
CoupleNet: Coupling Global Structure with Local Parts for Object Detection
intro: ICCV 2017
arxiv: https://arxiv.org/abs/1708.02863
Incremental Learning of Object Detectors without Catastrophic Forgetting
intro: ICCV 2017. Inria
arxiv: https://arxiv.org/abs/1708.06977
Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection
arxiv: https://arxiv.org/abs/1709.04347
StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection
arxiv: https://arxiv.org/abs/1709.05788
Dynamic Zoom-in Network for Fast Object Detection in Large Images
https://arxiv.org/abs/1711.05187
Zero-Annotation Object Detection with Web Knowledge Transfer
intro: NTU, Singapore & Amazon
keywords: multi-instance multi-label domain adaption learning framework
arxiv: https://arxiv.org/abs/1711.05954
MegDet
MegDet: A Large Mini-Batch Object Detector
intro: Peking University & Tsinghua University & Megvii Inc
arxiv: https://arxiv.org/abs/1711.07240
Single-Shot Refinement Neural Network for Object Detection
arxiv: https://arxiv.org/abs/1711.06897
github: https://github.com/sfzhang15/RefineDet
github: https://github.com/MTCloudVision/RefineDet-Mxnet
Receptive Field Block Net for Accurate and Fast Object Detection
intro: RFBNet
arxiv: https://arxiv.org/abs/1711.07767
github: https://github.com//ruinmessi/RFBNet
An Analysis of Scale Invariance in Object Detection - SNIP
intro: CVPR 2018
arxiv: https://arxiv.org/abs/1711.08189
github: https://github.com/bharatsingh430/snip
Feature Selective Networks for Object Detection
arxiv: https://arxiv.org/abs/1711.08879
Learning a Rotation Invariant Detector with Rotatable Bounding Box
arxiv: https://arxiv.org/abs/1711.09405
github(official, Caffe): https://github.com/liulei01/DRBox
Scalable Object Detection for Stylized Objects
intro: Microsoft AI & Research Munich
arxiv: https://arxiv.org/abs/1711.09822
Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids
arxiv: https://arxiv.org/abs/1712.00886
github: https://github.com/szq0214/GRP-DSOD
Deep Regionlets for Object Detection
keywords: region selection network, gating network
arxiv: https://arxiv.org/abs/1712.02408
Training and Testing Object Detectors with Virtual Images
intro: IEEE/CAA Journal of Automatica Sinica
arxiv: https://arxiv.org/abs/1712.08470
Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video
keywords: object mining, object tracking, unsupervised object discovery by appearance-based clustering, self-supervised detector adaptation
arxiv: https://arxiv.org/abs/1712.08832
Spot the Difference by Object Detection
intro: Tsinghua University & JD Group
arxiv: https://arxiv.org/abs/1801.01051
Localization-Aware Active Learning for Object Detection
arxiv: https://arxiv.org/abs/1801.05124
Object Detection with Mask-based Feature Encoding
arxiv: https://arxiv.org/abs/1802.03934
LSTD: A Low-Shot Transfer Detector for Object Detection
intro: AAAI 2018
arxiv: https://arxiv.org/abs/1803.01529
Domain Adaptive Faster R-CNN for Object Detection in the Wild
intro: CVPR 2018. ETH Zurich & ESAT/PSI
arxiv: https://arxiv.org/abs/1803.03243
github(official. Caffe): https://github.com/yuhuayc/da-faster-rcnn
Pseudo Mask Augmented Object Detection
arxiv: https://arxiv.org/abs/1803.05858
Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
intro: ECCV 2018
keywords: DCR V1
arxiv: https://arxiv.org/abs/1803.06799
github(official, MXNet): https://github.com/bowenc0221/Decoupled-Classification-Refinement
Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection
keywords: DCR V2
arxiv: https://arxiv.org/abs/1810.04002
github(official, MXNet): https://github.com/bowenc0221/Decoupled-Classification-Refinement
Learning Region Features for Object Detection
intro: Peking University & MSRA
arxiv: https://arxiv.org/abs/1803.07066
Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection
intro: Singapore Management University & Zhejiang University
arxiv: https://arxiv.org/abs/1803.08208
Object Detection for Comics using Manga109 Annotations
intro: University of Tokyo & National Institute of Informatics, Japan
arxiv: https://arxiv.org/abs/1803.08670
Task-Driven Super Resolution: Object Detection in Low-resolution Images
arxiv: https://arxiv.org/abs/1803.11316
Transferring Common-Sense Knowledge for Object Detection
arxiv: https://arxiv.org/abs/1804.01077
Multi-scale Location-aware Kernel Representation for Object Detection
intro: CVPR 2018
arxiv: https://arxiv.org/abs/1804.00428
github: https://github.com/Hwang64/MLKP
Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
intro: National University of Defense Technology
arxiv: https://arxiv.org/abs/1804.04606
DetNet: A Backbone network for Object Detection
intro: Tsinghua University & Megvii Inc
arxiv: https://arxiv.org/abs/1804.06215
Robust Physical Adversarial Attack on Faster R-CNN Object Detector
arxiv: https://arxiv.org/abs/1804.05810
AdvDetPatch: Attacking Object Detectors with Adversarial Patches
arxiv: https://arxiv.org/abs/1806.02299
Attacking Object Detectors via Imperceptible Patches on Background
https://arxiv.org/abs/1809.05966
Physical Adversarial Examples for Object Detectors
intro: WOOT 2018
arxiv: https://arxiv.org/abs/1807.07769
Quantization Mimic: Towards Very Tiny CNN for Object Detection
arxiv: https://arxiv.org/abs/1805.02152
Object detection at 200 Frames Per Second
intro: United Technologies Research Center-Ireland
arxiv: https://arxiv.org/abs/1805.06361
Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images
intro: CVPR 2018 Deep Vision Workshop
arxiv: https://arxiv.org/abs/1805.11778
SNIPER: Efficient Multi-Scale Training
intro: University of Maryland
keywords: SNIPER (Scale Normalization for Image Pyramid with Efficient Resampling)
arxiv: https://arxiv.org/abs/1805.09300
github: https://github.com/mahyarnajibi/SNIPER
Soft Sampling for Robust Object Detection
arxiv: https://arxiv.org/abs/1806.06986
MetaAnchor: Learning to Detect Objects with Customized Anchors
intro: Megvii Inc (Face++) & Fudan University
arxiv: https://arxiv.org/abs/1807.00980
Localization Recall Precision (LRP): A New Performance Metric for Object Detection
intro: ECCV 2018. Middle East Technical University
arxiv: https://arxiv.org/abs/1807.01696
github: https://github.com/cancam/LRP
Auto-Context R-CNN
intro: Rejected by ECCV18
arxiv: https://arxiv.org/abs/1807.02842
Pooling Pyramid Network for Object Detection
intro: Google AI Perception
arxiv: https://arxiv.org/abs/1807.03284
Modeling Visual Context is Key to Augmenting Object Detection Datasets
intro: ECCV 2018
arxiv: https://arxiv.org/abs/1807.07428
Dual Refinement Network for Single-Shot Object Detection
arxiv: https://arxiv.org/abs/1807.08638
Acquisition of Localization Confidence for Accurate Object Detection
intro: ECCV 2018
arxiv: https://arxiv.org/abs/1807.11590
gihtub: https://github.com/vacancy/PreciseRoIPooling
CornerNet: Detecting Objects as Paired Keypoints
intro: ECCV 2018
keywords: IoU-Net, PreciseRoIPooling
arxiv: https://arxiv.org/abs/1808.01244
github: https://github.com/umich-vl/CornerNet
Unsupervised Hard Example Mining from Videos for Improved Object Detection
intro: ECCV 2018
arxiv: https://arxiv.org/abs/1808.04285
SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection
arxiv: https://arxiv.org/abs/1808.04974
A Survey of Modern Object Detection Literature using Deep Learning
arxiv: https://arxiv.org/abs/1808.07256
Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages
intro: BMVC 2018
arxiv: https://arxiv.org/abs/1807.11013
github: https://github.com/lyxok1/Tiny-DSOD
Deep Feature Pyramid Reconfiguration for Object Detection
intro: ECCV 2018
arxiv: https://arxiv.org/abs/1808.07993
MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection
intro: ICPR 2018
arxiv: https://arxiv.org/abs/1809.01791
Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks
https://arxiv.org/abs/1809.03193
Deep Learning for Generic Object Detection: A Survey
https://arxiv.org/abs/1809.02165
Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples
intro: ICLR 2018
arxiv: https://github.com/alinlab/Confident_classifier
ScratchDet:Exploring to Train Single-Shot Object Detectors from Scratch
arxiv: https://arxiv.org/abs/1810.08425
github: https://github.com/KimSoybean/ScratchDet
Fast and accurate object detection in high resolution 4K and 8K video using GPUs
intro: Best Paper Finalist at IEEE High Performance Extreme Computing Conference (HPEC) 2018
intro: Carnegie Mellon University
arxiv: https://arxiv.org/abs/1810.10551
Hybrid Knowledge Routed Modules for Large-scale Object Detection
intro: NIPS 2018
arxiv: https://arxiv.org/abs/1810.12681
github(official, PyTorch): https://github.com/chanyn/HKRM
Gradient Harmonized Single-stage Detector
intro: AAAI 2019
arxiv: https://arxiv.org/abs/1811.05181
M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
intro: AAAI 2019
arxiv: https://arxiv.org/abs/1811.04533
github: https://github.com/qijiezhao/M2Det
BAN: Focusing on Boundary Context for Object Detection
arxiv：https://arxiv.org/abs/1811.05243
Multi-layer Pruning Framework for Compressing Single Shot MultiBox Detector
intro: WACV 2019
arxiv: https://arxiv.org/abs/1811.08342
R2CNN++: Multi-Dimensional Attention Based Rotation Invariant Detector with Robust Anchor Strategy
arxiv: https://arxiv.org/abs/1811.07126
github: https://github.com/DetectionTeamUCAS/R2CNN-Plus-Plus_Tensorflow
DeRPN: Taking a further step toward more general object detection
intro: AAAI 2019
intro: South China University of Technology
arxiv: https://arxiv.org/abs/1811.06700
github: https://github.com/HCIILAB/DeRPN
Fast Efficient Object Detection Using Selective Attention
arxiv：https://arxiv.org/abs/1811.07502
Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects
arxiv：https://arxiv.org/abs/1811.10862
Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects
arxiv：https://arxiv.org/abs/1811.12152
Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection
arxiv：https://arxiv.org/abs/1811.11318
Grid R-CNN
intro: SenseTime
arxiv: https://arxiv.org/abs/1811.12030
Transferable Adversarial Attacks for Image and Video Object Detection
-arxiv：https://arxiv.org/abs/1811.12641
Anchor Box Optimization for Object Detection
intro: University of Illinois at Urbana-Champaign & Microsoft Research
arxiv: https://arxiv.org/abs/1812.00469
AutoFocus: Efficient Multi-Scale Inference
intro: University of Maryland
arxiv: https://arxiv.org/abs/1812.01600
###Few-shot Object Detection via Feature Reweighting
arxiv：https://arxiv.org/abs/1812.01866
Practical Adversarial Attack Against Object Detector
arxiv：https://arxiv.org/abs/1812.10217
Learning Efficient Detector with Semi-supervised Adaptive Distillation
intro: SenseTime Research
arxiv: https://arxiv.org/abs/1901.00366
github: https://github.com/Tangshitao/Semi-supervised-Adaptive-Distillation
Scale-Aware Trident Networks for Object Detection
intro: University of Chinese Academy of Sciences & TuSimple
arxiv: https://arxiv.org/abs/1901.01892
github: https://github.com/TuSimple/simpledet
Region Proposal by Guided Anchoring
intro: CUHK - SenseTime Joint Lab & Amazon Rekognition & Nanyang Technological University
arxiv: https://arxiv.org/abs/1901.03278
Consistent Optimization for Single-Shot Object Detection
arxiv: https://arxiv.org/abs/1901.06563
blog: https://zhuanlan.zhihu.com/p/55416312
Bottom-up Object Detection by Grouping Extreme and Center Points
keywords: ExtremeNet
arxiv: https://arxiv.org/abs/1901.08043
github: https://github.com/xingyizhou/ExtremeNet
A Single-shot Object Detector with Feature Aggragation and Enhancement
arxiv: https://arxiv.org/abs/1902.02923
Bag of Freebies for Training Object Detection Neural Networks
intro: Amazon Web Services
arxiv: https://arxiv.org/abs/1902.04103
Non-Maximum Suppression (NMS)
End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression
intro: CVPR 2015
arxiv: http://arxiv.org/abs/1411.5309
paper: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wan_End-to-End_Integration_of_2015_CVPR_paper.pdf
A convnet for non-maximum suppression
arxiv: http://arxiv.org/abs/1511.06437
Improving Object Detection With One Line of Code
Soft-NMS – Improving Object Detection With One Line of Code
intro: ICCV 2017. University of Maryland
keywords: Soft-NMS
arxiv: https://arxiv.org/abs/1704.04503
github: https://github.com/bharatsingh430/soft-nms
Learning non-maximum suppression
intro: CVPR 2017
project page: https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/object-recognition-and-scene-understanding/learning-nms/
arxiv: https://arxiv.org/abs/1705.02950
github: https://github.com/hosang/gossipnet
Relation Networks for Object Detection
intro: CVPR 2018 oral
arxiv: https://arxiv.org/abs/1711.11575
github(official, MXNet): https://github.com/msracver/Relation-Networks-for-Object-Detection
Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes
keywords: Pairwise-NMS
arxiv: https://arxiv.org/abs/1901.03796
Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples
arxiv: https://arxiv.org/abs/1902.02067
Adversarial Examples
Adversarial Examples that Fool Detectors
intro: University of Illinois
arxiv: https://arxiv.org/abs/1712.02494
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
project page: http://nicholas.carlini.com/code/nn_breaking_detection/
arxiv: https://arxiv.org/abs/1705.07263
github: https://github.com/carlini/nn_breaking_detection
Weakly Supervised Object Detection
Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection
intro: CVPR 2016
arxiv: http://arxiv.org/abs/1604.05766
Weakly supervised object detection using pseudo-strong labels
arxiv: http://arxiv.org/abs/1607.04731
Saliency Guided End-to-End Learning for Weakly Supervised Object Detection
intro: IJCAI 2017
arxiv: https://arxiv.org/abs/1706.06768
Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection
intro: TPAMI 2017. National Institutes of Health (NIH) Clinical Center
arxiv: https://arxiv.org/abs/1801.03145
Video Object Detection
Learning Object Class Detectors from Weakly Annotated Video
intro: CVPR 2012
paper: https://www.vision.ee.ethz.ch/publications/papers/proceedings/eth_biwi_00905.pdf
Analysing domain shift factors between videos and images for object detection
arxiv: https://arxiv.org/abs/1501.01186
Video Object Recognition
slides: http://vision.princeton.edu/courses/COS598/2015sp/slides/VideoRecog/Video Object Recognition.pptx
Deep Learning for Saliency Prediction in Natural Video
intro: Submitted on 12 Jan 2016
keywords: Deep learning, saliency map, optical flow, convolution network, contrast features
paper: https://hal.archives-ouvertes.fr/hal-01251614/document
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
intro: Winning solution in ILSVRC2015 Object Detection from Video(VID) Task
arxiv: http://arxiv.org/abs/1604.02532
github: https://github.com/myfavouritekk/T-CNN
Object Detection from Video Tubelets with Convolutional Neural Networks
intro: CVPR 2016 Spotlight paper
arxiv: https://arxiv.org/abs/1604.04053
paper: http://www.ee.cuhk.edu.hk/~wlouyang/Papers/KangVideoDet_CVPR16.pdf
gihtub: https://github.com/myfavouritekk/vdetlib
Object Detection in Videos with Tubelets and Multi-context Cues
intro: SenseTime Group
slides: http://www.ee.cuhk.edu.hk/~xgwang/CUvideo.pdf
slides: http://image-net.org/challenges/talks/Object Detection in Videos with Tubelets and Multi-context Cues - Final.pdf
Context Matters: Refining Object Detection in Video with Recurrent Neural Networks
intro: BMVC 2016
keywords: pseudo-labeler
arxiv: http://arxiv.org/abs/1607.04648
paper: http://vision.cornell.edu/se3/wp-content/uploads/2016/07/video_object_detection_BMVC.pdf
CNN Based Object Detection in Large Video Images
intro: WangTao @ 愛奇藝
keywords: object retrieval, object detection, scene classification
slides: http://on-demand.gputechconf.com/gtc/2016/presentation/s6362-wang-tao-cnn-based-object-detection-large-video-images.pdf
Object Detection in Videos with Tubelet Proposal Networks
arxiv: https://arxiv.org/abs/1702.06355
Flow-Guided Feature Aggregation for Video Object Detection
intro: MSRA
arxiv: https://arxiv.org/abs/1703.10025
Video Object Detection using Faster R-CNN
blog: http://andrewliao11.github.io/object_detection/faster_rcnn/
github: https://github.com/andrewliao11/py-faster-rcnn-imagenet
Improving Context Modeling for Video Object Detection and Tracking
http://image-net.org/challenges/talks_2017/ilsvrc2017_short(poster).pdf
Temporal Dynamic Graph LSTM for Action-driven Video Object Detection
intro: ICCV 2017
arxiv: https://arxiv.org/abs/1708.00666
Mobile Video Object Detection with Temporally-Aware Feature Maps
arxiv: https://arxiv.org/abs/1711.06368
Towards High Performance Video Object Detection
arxiv: https://arxiv.org/abs/1711.11577
Impression Network for Video Object Detection
arxiv: https://arxiv.org/abs/1712.05896
Spatial-Temporal Memory Networks for Video Object Detection
arxiv: https://arxiv.org/abs/1712.06317
3D-DETNet: a Single Stage Video-Based Vehicle Detector
arxiv: https://arxiv.org/abs/1801.01769
Object Detection in Videos by Short and Long Range Object Linking
arxiv: https://arxiv.org/abs/1801.09823
Object Detection in Video with Spatiotemporal Sampling Networks
intro: University of Pennsylvania, 2Dartmouth College
arxiv: https://arxiv.org/abs/1803.05549
Towards High Performance Video Object Detection for Mobiles
intro: Microsoft Research Asia
arxiv: https://arxiv.org/abs/1804.05830
Optimizing Video Object Detection via a Scale-Time Lattice
intro: CVPR 2018
project page: http://mmlab.ie.cuhk.edu.hk/projects/ST-Lattice/
arxiv: https://arxiv.org/abs/1804.05472
github: https://github.com/hellock/scale-time-lattice
Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing
https://arxiv.org/abs/1809.01701
Fast Object Detection in Compressed Video
arxiv：https://arxiv.org/abs/1811.11057
Tube-CNN: Modeling temporal evolution of appearance for object detection in video
intro: INRIA/ENS
arxiv: https://arxiv.org/abs/1812.02619
AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling
intro: SysML 2019 oral
arxiv: https://arxiv.org/abs/1902.02910
Object Detection on Mobile Devices
Pelee: A Real-Time Object Detection System on Mobile Devices
intro: ICLR 2018 workshop track
intro: based on the SSD
arxiv: https://arxiv.org/abs/1804.06882
github: https://github.com/Robert-JunWang/Pelee
Object Detection in 3D
Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
arxiv: https://arxiv.org/abs/1609.06666
Complex-YOLO: Real-time 3D Object Detection on Point Clouds
intro: Valeo Schalter und Sensoren GmbH & Ilmenau University of Technology
arxiv: https://arxiv.org/abs/1803.06199
Focal Loss in 3D Object Detection
arxiv: https://arxiv.org/abs/1809.06065
github: https://github.com/pyun-ram/FL3D
3D Object Detection Using Scale Invariant and Feature Reweighting Networks
intro: AAAI 2019
arxiv: https://arxiv.org/abs/1901.02237
3D Backbone Network for 3D Object Detection
arxiv: https://arxiv.org/abs/1901.08373
Object Detection on RGB-D
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
arxiv: http://arxiv.org/abs/1407.5736
Differential Geometry Boosts Convolutional Neural Networks for Object Detection
intro: CVPR 2016
paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w23/html/Wang_Differential_Geometry_Boosts_CVPR_2016_paper.html
A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation
arxiv: https://arxiv.org/abs/1703.03347
Zero-Shot Object Detection
Zero-Shot Detection
intro: Australian National University
keywords: YOLO
arxiv: https://arxiv.org/abs/1803.07113
Zero-Shot Object Detection
arxiv: https://arxiv.org/abs/1804.04340
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
intro: Australian National University
arxiv: https://arxiv.org/abs/1803.06049
Zero-Shot Object Detection by Hybrid Region Embedding
intro: Middle East Technical University & Hacettepe University
arxiv: https://arxiv.org/abs/1805.06157
Salient Object Detection
This task involves predicting the salient regions of an image given by human eye fixations.
Best Deep Saliency Detection Models (CVPR 2016 & 2015)
page: http://i.cs.hku.hk/~yzyu/vision.html
Large-scale optimization of hierarchical features for saliency prediction in natural images
paper: http://coxlab.org/pdfs/cvpr2014_vig_saliency.pdf
Predicting Eye Fixations using Convolutional Neural Networks
paper: http://www.escience.cn/system/file?fileId=72648
Saliency Detection by Multi-Context Deep Learning
paper: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhao_Saliency_Detection_by_2015_CVPR_paper.pdf
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
arxiv: http://arxiv.org/abs/1510.05484
SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection
paper: www.shengfenghe.com/supercnn-a-superpixelwise-convolutional-neural-network-for-salient-object-detection.html
Shallow and Deep Convolutional Networks for Saliency Prediction
intro: CVPR 2016
arxiv: http://arxiv.org/abs/1603.00845
github: https://github.com/imatge-upc/saliency-2016-cvpr
Recurrent Attentional Networks for Saliency Detection
intro: CVPR 2016. recurrent attentional convolutional-deconvolution network (RACDNN)
arxiv: http://arxiv.org/abs/1604.03227
Two-Stream Convolutional Networks for Dynamic Saliency Prediction
arxiv: http://arxiv.org/abs/1607.04730
Unconstrained Salient Object Detection
Unconstrained Salient Object Detection via Proposal Subset Optimization
intro: CVPR 2016
project page: http://cs-people.bu.edu/jmzhang/sod.html
paper: http://cs-people.bu.edu/jmzhang/SOD/CVPR16SOD_camera_ready.pdf
github: https://github.com/jimmie33/SOD
caffe model zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo
DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection
paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_DHSNet_Deep_Hierarchical_CVPR_2016_paper.pdf
Salient Object Subitizing
intro: CVPR 2015
intro: predicting the existence and the number of salient objects in an image using holistic cues
project page: http://cs-people.bu.edu/jmzhang/sos.html
arxiv: http://arxiv.org/abs/1607.07525
paper: http://cs-people.bu.edu/jmzhang/SOS/SOS_preprint.pdf
caffe model zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo
Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection
intro: ACMMM 2016. deeply-supervised recurrent convolutional neural network (DSRCNN)
arxiv: http://arxiv.org/abs/1608.05177
Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs
intro: ECCV 2016
arxiv: http://arxiv.org/abs/1608.05186
Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection
arxiv: http://arxiv.org/abs/1608.08029
A Deep Multi-Level Network for Saliency Prediction
arxiv: http://arxiv.org/abs/1609.01064
Visual Saliency Detection Based on Multiscale Deep CNN Features
intro: IEEE Transactions on Image Processing
arxiv: http://arxiv.org/abs/1609.02077
A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection
intro: DSCLRCN
arxiv: https://arxiv.org/abs/1610.01708
Deeply supervised salient object detection with short connections
intro: IEEE TPAMI 2018 (IEEE CVPR 2017)
arxiv: https://arxiv.org/abs/1611.04849
github(official, Caffe): https://github.com/Andrew-Qibin/DSS
github(Tensorflow): https://github.com/Joker316701882/Salient-Object-Detection
Weakly Supervised Top-down Salient Object Detection
intro: Nanyang Technological University
arxiv: https://arxiv.org/abs/1611.05345
SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
project page: https://imatge-upc.github.io/saliency-salgan-2017/
arxiv: https://arxiv.org/abs/1701.01081
Visual Saliency Prediction Using a Mixture of Deep Neural Networks
arxiv: https://arxiv.org/abs/1702.00372
A Fast and Compact Salient Score Regression Network Based on Fully Convolutional Network
arxiv: https://arxiv.org/abs/1702.00615
Saliency Detection by Forward and Backward Cues in Deep-CNNs
arxiv: https://arxiv.org/abs/1703.00152
Supervised Adversarial Networks for Image Saliency Detection
arxiv: https://arxiv.org/abs/1704.07242
Group-wise Deep Co-saliency Detection
arxiv: https://arxiv.org/abs/1707.07381
Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection
intro: University of Maryland College Park & eBay Inc
arxiv: https://arxiv.org/abs/1708.00079
Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection
intro: ICCV 2017
arixv: https://arxiv.org/abs/1708.02001
Learning Uncertain Convolutional Features for Accurate Saliency Detection
intro: Accepted as a poster in ICCV 2017
arxiv: https://arxiv.org/abs/1708.02031
Deep Edge-Aware Saliency Detection
arxiv: https://arxiv.org/abs/1708.04366
Self-explanatory Deep Salient Object Detection
intro: National University of Defense Technology, China & National University of Singapore
arxiv: https://arxiv.org/abs/1708.05595
PiCANet: Learning Pixel-wise Contextual Attention in ConvNets and Its Application in Saliency Detection
arxiv: https://arxiv.org/abs/1708.06433
DeepFeat: A Bottom Up and Top Down Saliency Model Based on Deep Features of Convolutional Neural Nets
arxiv: https://arxiv.org/abs/1709.02495
Recurrently Aggregating Deep Features for Salient Object Detection
intro: AAAI 2018
paper: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16775/16281
Deep saliency: What is learnt by a deep network about saliency?
intro: 2nd Workshop on Visualisation for Deep Learning in the 34th International Conference On Machine Learning
arxiv: https://arxiv.org/abs/1801.04261
Contrast-Oriented Deep Neural Networks for Salient Object Detection
intro: TNNLS
arxiv: https://arxiv.org/abs/1803.11395
Salient Object Detection by Lossless Feature Reflection
intro: IJCAI 2018
arxiv: https://arxiv.org/abs/1802.06527
HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection
arxiv: https://arxiv.org/abs/1804.05142
Video Saliency Detection
Deep Learning For Video Saliency Detection
arxiv: https://arxiv.org/abs/1702.00871
Video Salient Object Detection Using Spatiotemporal Deep Features
arxiv: https://arxiv.org/abs/1708.01447
Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM
arxiv: https://arxiv.org/abs/1709.06316
Visual Relationship Detection
Visual Relationship Detection with Language Priors
intro: ECCV 2016 oral
paper: https://cs.stanford.edu/people/ranjaykrishna/vrd/vrd.pdf
github: https://github.com/Prof-Lu-Cewu/Visual-Relationship-Detection
ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection
intro: Visual Phrase reasoning Convolutional Neural Network (ViP-CNN), Visual Phrase Reasoning Structure (VPRS)
arxiv: https://arxiv.org/abs/1702.07191
Visual Translation Embedding Network for Visual Relation Detection
arxiv: https://www.arxiv.org/abs/1702.08319
Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection
intro: CVPR 2017 spotlight paper
arxiv: https://arxiv.org/abs/1703.03054
Detecting Visual Relationships with Deep Relational Networks
intro: CVPR 2017 oral. The Chinese University of Hong Kong
arxiv: https://arxiv.org/abs/1704.03114
Identifying Spatial Relations in Images using Convolutional Neural Networks
arxiv: https://arxiv.org/abs/1706.04215
PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN
intro: ICCV
arxiv: https://arxiv.org/abs/1708.01956
Natural Language Guided Visual Relationship Detection
arxiv: https://arxiv.org/abs/1711.06032
Detecting Visual Relationships Using Box Attention
intro: Google AI & IST Austria
arxiv: https://arxiv.org/abs/1807.02136
Google AI Open Images - Visual Relationship Track
intro: Detect pairs of objects in particular relationships
kaggle: https://www.kaggle.com/c/google-ai-open-images-visual-relationship-track
Context-Dependent Diffusion Network for Visual Relationship Detection
intro: 2018 ACM Multimedia Conference
arxiv: https://arxiv.org/abs/1809.06213
A Problem Reduction Approach for Visual Relationships Detection
intro: ECCV 2018 Workshop
arxiv: https://arxiv.org/abs/1809.09828
Face Deteciton
Multi-view Face Detection Using Deep Convolutional Neural Networks
intro: Yahoo
arxiv: http://arxiv.org/abs/1502.02766
github: https://github.com/guoyilin/FaceDetection_CNN
From Facial Parts Responses to Face Detection: A Deep Learning Approach
intro: ICCV 2015. CUHK
project page: http://personal.ie.cuhk.edu.hk/~ys014/projects/Faceness/Faceness.html
arxiv: https://arxiv.org/abs/1509.06451
paper: http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Yang_From_Facial_Parts_ICCV_2015_paper.pdf
Compact Convolutional Neural Network Cascade for Face Detection
arxiv: http://arxiv.org/abs/1508.01292
github: https://github.com/Bkmz21/FD-Evaluation
github: https://github.com/Bkmz21/CompactCNNCascade
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
intro: ECCV 2016
arxiv: https://arxiv.org/abs/1606.00850
github(MXNet): https://github.com/tfwu/FaceDetection-ConvNet-3D
CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection
intro: CMU
arxiv: https://arxiv.org/abs/1606.05413
Towards a Deep Learning Framework for Unconstrained Face Detection
intro: overlap with CMS-RCNN
arxiv: https://arxiv.org/abs/1612.05322
Supervised Transformer Network for Efficient Face Detection
arxiv: http://arxiv.org/abs/1607.05477
UnitBox: An Advanced Object Detection Network
intro: ACM MM 2016
keywords: IOULoss
arxiv: http://arxiv.org/abs/1608.01471
Bootstrapping Face Detection with Hard Negative Examples
author: 萬韶華 @ 小米.
intro: Faster R-CNN, hard negative mining. state-of-the-art on the FDDB dataset
arxiv: http://arxiv.org/abs/1608.02236
Grid Loss: Detecting Occluded Faces
intro: ECCV 2016
arxiv: https://arxiv.org/abs/1609.00129
paper: http://lrs.icg.tugraz.at/pubs/opitz_eccv_16.pdf
poster: http://www.eccv2016.org/files/posters/P-2A-34.pdf
A Multi-Scale Cascade Fully Convolutional Network Face Detector
intro: ICPR 2016
arxiv: http://arxiv.org/abs/1609.03536
MTCNN
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks
project page: https://kpzhang93.github.io/MTCNN_face_detection_alignment/index.html
arxiv: https://arxiv.org/abs/1604.02878
github(official, Matlab): https://github.com/kpzhang93/MTCNN_face_detection_alignment
github: https://github.com/pangyupo/mxnet_mtcnn_face_detection
github: https://github.com/DaFuCoding/MTCNN_Caffe
github(MXNet): https://github.com/Seanlinx/mtcnn
github: https://github.com/Pi-DeepLearning/RaspberryPi-FaceDetection-MTCNN-Caffe-With-Motion
github(Caffe): https://github.com/foreverYoungGitHub/MTCNN
github: https://github.com/CongWeilin/mtcnn-caffe
github(OpenCV+OpenBlas): https://github.com/AlphaQi/MTCNN-light
github(Tensorflow+golang): https://github.com/jdeng/goface
Face Detection using Deep Learning: An Improved Faster RCNN Approach
intro: DeepIR Inc
arxiv: https://arxiv.org/abs/1701.08289
Faceness-Net: Face Detection through Deep Facial Part Responses
intro: An extended version of ICCV 2015 paper
arxiv: https://arxiv.org/abs/1701.08393
Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained “Hard Faces”
intro: CVPR 2017. MP-RCNN, MP-RPN
arxiv: https://arxiv.org/abs/1703.09145
End-To-End Face Detection and Recognition
arxiv: https://arxiv.org/abs/1703.10818
Face R-CNN
arxiv: https://arxiv.org/abs/1706.01061
Face Detection through Scale-Friendly Deep Convolutional Networks
arxiv: https://arxiv.org/abs/1706.02863
Scale-Aware Face Detection
intro: CVPR 2017. SenseTime & Tsinghua University
arxiv: https://arxiv.org/abs/1706.09876
Detecting Faces Using Inside Cascaded Contextual CNN
intro: CVPR 2017. Tencent AI Lab & SenseTime
paper: http://ai.tencent.com/ailab/media/publications/Detecting_Faces_Using_Inside_Cascaded_Contextual_CNN.pdf
Multi-Branch Fully Convolutional Network for Face Detection
arxiv: https://arxiv.org/abs/1707.06330
SSH: Single Stage Headless Face Detector
intro: ICCV 2017. University of Maryland
arxiv: https://arxiv.org/abs/1708.03979
github(official, Caffe): https://github.com/mahyarnajibi/SSH
Dockerface: an easy to install and use Faster R-CNN face detector in a Docker container
arxiv: https://arxiv.org/abs/1708.04370
FaceBoxes: A CPU Real-time Face Detector with High Accuracy
intro: IJCB 2017
keywords: Rapidly Digested Convolutional Layers (RDCL), Multiple Scale Convolutional Layers (MSCL)
intro: the proposed detector runs at 20 FPS on a single CPU core and 125 FPS using a GPU for VGA-resolution images
arxiv: https://arxiv.org/abs/1708.05234
github(official): https://github.com/sfzhang15/FaceBoxes
github(Caffe): https://github.com/zeusees/FaceBoxes
S3FD: Single Shot Scale-invariant Face Detector
intro: ICCV 2017. Chinese Academy of Sciences
intro: can run at 36 FPS on a Nvidia Titan X (Pascal) for VGA-resolution images
arxiv: https://arxiv.org/abs/1708.05237
github(Caffe, official): https://github.com/sfzhang15/SFD
github: https://github.com//clcarwin/SFD_pytorch
Detecting Faces Using Region-based Fully Convolutional Networks
arxiv: https://arxiv.org/abs/1709.05256
AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection
arxiv: https://arxiv.org/abs/1709.07326
Face Attention Network: An effective Face Detector for the Occluded Faces
arxiv: https://arxiv.org/abs/1711.07246
Feature Agglomeration Networks for Single Stage Face Detection
arxiv: https://arxiv.org/abs/1712.00721
Face Detection Using Improved Faster RCNN
intro: Huawei Cloud BU
arxiv: https://arxiv.org/abs/1802.02142
PyramidBox: A Context-assisted Single Shot Face Detector
intro: Baidu, Inc
arxiv: https://arxiv.org/abs/1803.07737
A Fast Face Detection Method via Convolutional Neural Network
intro: Neurocomputing
arxiv: https://arxiv.org/abs/1803.10103
Beyond Trade-off: Accelerate FCN-based Face Detector with Higher Accuracy
intro: CVPR 2018. Beihang University & CUHK & Sensetime
arxiv: https://arxiv.org/abs/1804.05197
Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks
intro: CVPR 2018
arxiv: https://arxiv.org/abs/1804.06039
github: https://github.com/Jack-CV/PCN
SFace: An Efficient Network for Face Detection in Large Scale Variations
intro: Beihang University & Megvii Inc. (Face++)
arxiv: https://arxiv.org/abs/1804.06559
Survey of Face Detection on Low-quality Images
arxiv: https://arxiv.org/abs/1804.07362
Anchor Cascade for Efficient Face Detection
intro: The University of Sydney
arxiv: https://arxiv.org/abs/1805.03363
Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization
intro: IEEE MMSP
arxiv: https://arxiv.org/abs/1805.12302
Selective Refinement Network for High Performance Face Detection
https://arxiv.org/abs/1809.02693
DSFD: Dual Shot Face Detector
arxiv：https://arxiv.org/abs/1810.10220
Learning Better Features for Face Detection with Feature Fusion and Segmentation Supervision
arxiv：https://arxiv.org/abs/1811.08557
FA-RPN: Floating Region Proposals for Face Detection
arxiv: https://arxiv.org/abs/1812.05586
Robust and High Performance Face Detector
https://arxiv.org/abs/1901.02350
DAFE-FD: Density Aware Feature Enrichment for Face Detection
arxiv: https://arxiv.org/abs/1901.05375
Improved Selective Refinement Network for Face Detection
intro: Chinese Academy of Sciences & JD AI Research
arxiv: https://arxiv.org/abs/1901.06651
Revisiting a single-stage method for face detection
arxiv: https://arxiv.org/abs/1902.01559
Detect Small Faces
Finding Tiny Faces
intro: CVPR 2017. CMU
project page: http://www.cs.cmu.edu/~peiyunh/tiny/index.html
arxiv: https://arxiv.org/abs/1612.04402
github(official, Matlab): https://github.com/peiyunh/tiny
github(inference-only): https://github.com/chinakook/hr101_mxnet
github: https://github.com/cydonia999/Tiny_Faces_in_Tensorflow
Detecting and counting tiny faces
intro: ENS Paris-Saclay. ExtendedTinyFaces
intro: Detecting and counting small objects - Analysis, review and application to counting
arxiv: https://arxiv.org/abs/1801.06504
github: https://github.com/alexattia/ExtendedTinyFaces
Seeing Small Faces from Robust Anchor’s Perspective
intro: CVPR 2018
arxiv: https://arxiv.org/abs/1802.09058
Face-MagNet: Magnifying Feature Maps to Detect Small Faces
intro: WACV 2018
keywords: Face Magnifier Network (Face-MageNet)
arxiv: https://arxiv.org/abs/1803.05258
github: https://github.com/po0ya/face-magnet
Robust Face Detection via Learning Small Faces on Hard Images
intro: Johns Hopkins University & Stanford University
arxiv: https://arxiv.org/abs/1811.11662
github: https://github.com/bairdzhang/smallhardface
SFA: Small Faces Attention Face Detector
intro: Jilin University
arxiv: https://arxiv.org/abs/1812.08402
Person Head Detection
Context-aware CNNs for person head detection
intro: ICCV 2015
project page: http://www.di.ens.fr/willow/research/headdetection/
arxiv: http://arxiv.org/abs/1511.07917
github: https://github.com/aosokin/cnn_head_detection
Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture
arxiv: https://arxiv.org/abs/1803.09256
A Comparison of CNN-based Face and Head Detectors for Real-Time Video Surveillance Applications
https://arxiv.org/abs/1809.03336
FCHD: A fast and accurate head detector
arxiv: https://arxiv.org/abs/1809.08766
github(PyTorch, official): https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-Detector
Pedestrian Detection / People Detection
Pedestrian Detection aided by Deep Learning Semantic Tasks
intro: CVPR 2015
project page: http://mmlab.ie.cuhk.edu.hk/projects/TA-CNN/
arxiv: http://arxiv.org/abs/1412.0069
Deep Learning Strong Parts for Pedestrian Detection
intro: ICCV 2015. CUHK. DeepParts
intro: Achieving 11.89% average miss rate on Caltech Pedestrian Dataset
paper: http://personal.ie.cuhk.edu.hk/~pluo/pdf/tianLWTiccv15.pdf
Taking a Deeper Look at Pedestrians
intro: CVPR 2015
arxiv: https://arxiv.org/abs/1501.05790
Convolutional Channel Features
intro: ICCV 2015
arxiv: https://arxiv.org/abs/1504.07339
github: https://github.com/byangderek/CCF
End-to-end people detection in crowded scenes
arxiv: http://arxiv.org/abs/1506.04878
github: https://github.com/Russell91/reinspect
ipn: http://nbviewer.ipython.org/github/Russell91/ReInspect/blob/master/evaluation_reinspect.ipynb
youtube: https://www.youtube.com/watch?v=QeWl0h3kQ24
Learning Complexity-Aware Cascades for Deep Pedestrian Detection
intro: ICCV 2015
arxiv: https://arxiv.org/abs/1507.05348
Deep convolutional neural networks for pedestrian detection
arxiv: http://arxiv.org/abs/1510.03608
github: https://github.com/DenisTome/DeepPed
Scale-aware Fast R-CNN for Pedestrian Detection
arxiv: https://arxiv.org/abs/1510.08160
New algorithm improves speed and accuracy of pedestrian detection
blog: http://www.eurekalert.org/pub_releases/2016-02/uoc–nai020516.php
Pushing the Limits of Deep CNNs for Pedestrian Detection
intro: “set a new record on the Caltech pedestrian dataset, lowering the log-average miss rate from 11.7% to 8.9%”
arxiv: http://arxiv.org/abs/1603.04525
A Real-Time Deep Learning Pedestrian Detector for Robot Navigation
arxiv: http://arxiv.org/abs/1607.04436
A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation
arxiv: http://arxiv.org/abs/1607.04441
Is Faster R-CNN Doing Well for Pedestrian Detection?
intro: ECCV 2016
arxiv: http://arxiv.org/abs/1607.07032
github: https://github.com/zhangliliang/RPN_BF/tree/RPN-pedestrian
Unsupervised Deep Domain Adaptation for Pedestrian Detection
intro: ECCV Workshop 2016
arxiv: https://arxiv.org/abs/1802.03269
Reduced Memory Region Based Deep Convolutional Neural Network Detection
intro: IEEE 2016 ICCE-Berlin
arxiv: http://arxiv.org/abs/1609.02500
Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection
arxiv: https://arxiv.org/abs/1610.03466
Detecting People in Artwork with CNNs
intro: ECCV 2016 Workshops
arxiv: https://arxiv.org/abs/1610.08871
Multispectral Deep Neural Networks for Pedestrian Detection
intro: BMVC 2016 oral
arxiv: https://arxiv.org/abs/1611.02644
Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection
arxiv: https://arxiv.org/abs/1902.05291
Deep Multi-camera People Detection
arxiv: https://arxiv.org/abs/1702.04593
Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters
intro: CVPR 2017
project page: http://ml.cs.tsinghua.edu.cn:5000/publications/synunity/
arxiv: https://arxiv.org/abs/1703.06283
github(Tensorflow): https://github.com/huangshiyu13/RPNplus
What Can Help Pedestrian Detection?
intro: CVPR 2017. Tsinghua University & Peking University & Megvii Inc.
keywords: Faster R-CNN, HyperLearner
arxiv: https://arxiv.org/abs/1705.02757
paper: http://openaccess.thecvf.com/content_cvpr_2017/papers/Mao_What_Can_Help_CVPR_2017_paper.pdf
Illuminating Pedestrians via Simultaneous Detection & Segmentation
arxiv: https://arxiv.org/abs/1706.08564
Rotational Rectification Network for Robust Pedestrian Detection
intro: CMU & Volvo Construction
arxiv: https://arxiv.org/abs/1706.08917
STD-PD: Generating Synthetic Training Data for Pedestrian Detection in Unannotated Videos
intro: The University of North Carolina at Chapel Hill
arxiv: https://arxiv.org/abs/1707.09100
Too Far to See? Not Really! — Pedestrian Detection with Scale-aware Localization Policy
arxiv: https://arxiv.org/abs/1709.00235
Repulsion Loss: Detecting Pedestrians in a Crowd
arxiv: https://arxiv.org/abs/1711.07752
Aggregated Channels Network for Real-Time Pedestrian Detection
arxiv: https://arxiv.org/abs/1801.00476
Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection
intro: State Key Lab of CAD&CG, Zhejiang University
arxiv: https://arxiv.org/abs/1803.05347
Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection
arxiv: https://arxiv.org/abs/1804.00872
Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond
arxiv: https://arxiv.org/abs/1804.02047
PCN: Part and Context Information for Pedestrian Detection with CNNs
intro: British Machine Vision Conference(BMVC) 2017
arxiv: https://arxiv.org/abs/1804.04483
Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation
intro: ECCV 2018. Hikvision Research Institute
arxiv: https://arxiv.org/abs/1807.01438
Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd
intro: ECCV 2018
arxiv: https://arxiv.org/abs/1807.08407
Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation
intro: BMVC 2018
arxiv: https://arxiv.org/abs/1808.04818
Pedestrian Detection with Autoregressive Network Phases
intro: Michigan State University
arxiv: https://arxiv.org/abs/1812.00440
The Cross-Modality Disparity Problem in Multispectral Pedestrian Detection
-arxiv: https://arxiv.org/abs/1901.02645
Vehicle Detection
DAVE: A Unified Framework for Fast Vehicle Detection and Annotation
intro: ECCV 2016
arxiv: http://arxiv.org/abs/1607.04564
Evolving Boxes for fast Vehicle Detection
arxiv: https://arxiv.org/abs/1702.00254
Fine-Grained Car Detection for Visual Census Estimation
intro: AAAI 2016
arxiv: https://arxiv.org/abs/1709.02480
SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection
intro: IEEE Transactions on Intelligent Transportation Systems (T-ITS)
arxiv: https://arxiv.org/abs/1804.00433
Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data
intro: UC Berkeley
arxiv: https://arxiv.org/abs/1808.08603
Domain Randomization for Scene-Specific Car Detection and Pose Estimation
arxiv：https://arxiv.org/abs/1811.05939
ShuffleDet: Real-Time Vehicle Detection Network in On-board Embedded UAV Imagery
intro: ECCV 2018, UAVision 2018
arxiv: https://arxiv.org/abs/1811.06318
Traffic-Sign Detection
Traffic-Sign Detection and Classification in the Wild
intro: CVPR 2016
project page(code+dataset): http://cg.cs.tsinghua.edu.cn/traffic-sign/
paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper.pdf
code & model: http://cg.cs.tsinghua.edu.cn/traffic-sign/data_model_code/newdata0411.zip
Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data
intro: CVPR 2017 workshop
paper: http://openaccess.thecvf.com/content_cvpr_2017_workshops/w9/papers/Jensen_Evaluating_State-Of-The-Art_Object_CVPR_2017_paper.pdf
Detecting Small Signs from Large Images
intro: IEEE Conference on Information Reuse and Integration (IRI) 2017 oral
arxiv: https://arxiv.org/abs/1706.08574
Localized Traffic Sign Detection with Multi-scale Deconvolution Networks
arxiv: https://arxiv.org/abs/1804.10428
Detecting Traffic Lights by Single Shot Detection
intro: ITSC 2018
arxiv: https://arxiv.org/abs/1805.02523
A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection
intro: IEEE 15th Conference on Computer and Robot Vision
arxiv: https://arxiv.org/abs/1806.07987
demo: https://www.youtube.com/watch?v=_YmogPzBXOw&feature=youtu.be
Skeleton Detection
Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs
arxiv: http://arxiv.org/abs/1603.09446
github: https://github.com/zeakey/DeepSkeleton
DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images
arxiv: http://arxiv.org/abs/1609.03659
SRN: Side-output Residual Network for Object Symmetry Detection in the Wild
intro: CVPR 2017
arxiv: https://arxiv.org/abs/1703.02243
github: https://github.com/KevinKecc/SRN
Hi-Fi: Hierarchical Feature Integration for Skeleton Detection
arxiv: https://arxiv.org/abs/1801.01849
Fruit Detection
Deep Fruit Detection in Orchards
arxiv: https://arxiv.org/abs/1610.03677
Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards
intro: The Journal of Field Robotics in May 2016
project page: http://confluence.acfr.usyd.edu.au/display/AGPub/
arxiv: https://arxiv.org/abs/1610.08120
Shadow Detection
Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network
arxiv: https://arxiv.org/abs/1709.09283
A+D-Net: Shadow Detection with Adversarial Shadow Attenuation
arxiv: https://arxiv.org/abs/1712.01361
Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal
arxiv: https://arxiv.org/abs/1712.02478
Direction-aware Spatial Context Features for Shadow Detection
intro: CVPR 2018
arxiv: https://arxiv.org/abs/1712.04142
Direction-aware Spatial Context Features for Shadow Detection and Removal
intro: The Chinese University of Hong Kong & The Hong Kong Polytechnic University
arxiv: https://arxiv.org/abs/1805.04635
Others Detection
Deep Deformation Network for Object Landmark Localization
arxiv: http://arxiv.org/abs/1605.01014
Fashion Landmark Detection in the Wild
intro: ECCV 2016
project page: http://personal.ie.cuhk.edu.hk/~lz013/projects/FashionLandmarks.html
arxiv: http://arxiv.org/abs/1608.03049
github(Caffe): https://github.com/liuziwei7/fashion-landmarks
Deep Learning for Fast and Accurate Fashion Item Detection
intro: Kuznech Inc.
intro: MultiBox and Fast R-CNN
paper: https://kddfashion2016.mybluemix.net/kddfashion_finalSubmissions/Deep Learning for Fast and Accurate Fashion Item Detection.pdf
OSMDeepOD - OSM and Deep Learning based Object Detection from Aerial Imagery (formerly known as “OSM-Crosswalk-Detection”)
github: https://github.com/geometalab/OSMDeepOD
Selfie Detection by Synergy-Constraint Based Convolutional Neural Network
intro: IEEE SITIS 2016
arxiv: https://arxiv.org/abs/1611.04357
Associative Embedding:End-to-End Learning for Joint Detection and Grouping
arxiv: https://arxiv.org/abs/1611.05424
Deep Cuboid Detection: Beyond 2D Bounding Boxes
intro: CMU & Magic Leap
arxiv: https://arxiv.org/abs/1611.10010
Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection
arxiv: https://arxiv.org/abs/1612.03019
Deep Learning Logo Detection with Data Expansion by Synthesising Context
arxiv: https://arxiv.org/abs/1612.09322
Scalable Deep Learning Logo Detection
arxiv: https://arxiv.org/abs/1803.11417
Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks
arxiv: https://arxiv.org/abs/1702.00307
Automatic Handgun Detection Alarm in Videos Using Deep Learning
arxiv: https://arxiv.org/abs/1702.05147
results: https://github.com/SihamTabik/Pistol-Detection-in-Videos
Objects as context for part detection
arxiv: https://arxiv.org/abs/1703.09529
Using Deep Networks for Drone Detection
intro: AVSS 2017
arxiv: https://arxiv.org/abs/1706.05726
Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection
intro: ICCV 2017
arxiv: https://arxiv.org/abs/1708.01642
Target Driven Instance Detection
arxiv: https://arxiv.org/abs/1803.04610
DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion
arxiv: https://arxiv.org/abs/1709.04577
VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
intro: ICCV 2017
arxiv: https://arxiv.org/abs/1710.06288
github: https://github.com/SeokjuLee/VPGNet
Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants
arxiv: https://arxiv.org/abs/1711.05128
ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos
intro: WACV 2018
arxiv: https://arxiv.org/abs/1801.02031
Deep Learning Object Detection Methods for Ecological Camera Trap Data
intro: Conference of Computer and Robot Vision. University of Guelph
arxiv: https://arxiv.org/abs/1803.10842
EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection
arxiv: https://arxiv.org/abs/1806.05525
Towards End-to-End Lane Detection: an Instance Segmentation Approach
arxiv: https://arxiv.org/abs/1802.05591
github: https://github.com/MaybeShewill-CV/lanenet-lane-detection
iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection
intro: BMVC 2018
project page: https://gaochen315.github.io/iCAN/
arxiv: https://arxiv.org/abs/1808.10437
github: https://github.com/vt-vl-lab/iCAN
Densely Supervised Grasp Detector (DSGD)
https://arxiv.org/abs/1810.03962
Object Proposal
DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers
arxiv: http://arxiv.org/abs/1510.04445
github: https://github.com/aghodrati/deepproposal
Scale-aware Pixel-wise Object Proposal Networks
intro: IEEE Transactions on Image Processing
arxiv: http://arxiv.org/abs/1601.04798
Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization
intro: BMVC 2016. AttractioNet
arxiv: https://arxiv.org/abs/1606.04446
github: https://github.com/gidariss/AttractioNet
Learning to Segment Object Proposals via Recursive Neural Networks
arxiv: https://arxiv.org/abs/1612.01057
Learning Detection with Diverse Proposals
intro: CVPR 2017
keywords: differentiable Determinantal Point Process (DPP) layer, Learning Detection with Diverse Proposals (LDDP)
arxiv: https://arxiv.org/abs/1704.03533
ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond
keywords: product detection
arxiv: https://arxiv.org/abs/1704.06752
Improving Small Object Proposals for Company Logo Detection
intro: ICMR 2017
arxiv: https://arxiv.org/abs/1704.08881
Open Logo Detection Challenge
intro: BMVC 2018
keywords: QMUL-OpenLogo
project page: https://qmul-openlogo.github.io/
arxiv: https://arxiv.org/abs/1807.01964
AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects
intro: ACCV 2018 oral
arxiv: https://arxiv.org/abs/1811.08728
github: https://github.com/chwilms/AttentionMask
Localization
Beyond Bounding Boxes: Precise Localization of Objects in Images
intro: PhD Thesis
homepage: http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-193.html
phd-thesis: http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-193.pdf
github(“SDS using hypercolumns”): https://github.com/bharath272/sds
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning
arxiv: http://arxiv.org/abs/1503.00949
Weakly Supervised Object Localization Using Size Estimates
arxiv: http://arxiv.org/abs/1608.04314
Active Object Localization with Deep Reinforcement Learning
intro: ICCV 2015
keywords: Markov Decision Process
arxiv: https://arxiv.org/abs/1511.06015
Localizing objects using referring expressions
intro: ECCV 2016
keywords: LSTM, multiple instance learning (MIL)
paper: http://www.umiacs.umd.edu/~varun/files/refexp-ECCV16.pdf
github: https://github.com/varun-nagaraja/referring-expressions
LocNet: Improving Localization Accuracy for Object Detection
intro: CVPR 2016 oral
arxiv: http://arxiv.org/abs/1511.07763
github: https://github.com/gidariss/LocNet
Learning Deep Features for Discriminative Localization
homepage: http://cnnlocalization.csail.mit.edu/
arxiv: http://arxiv.org/abs/1512.04150
github(Tensorflow): https://github.com/jazzsaxmafia/Weakly_detector
github: https://github.com/metalbubble/CAM
github: https://github.com/tdeboissiere/VGG16CAM-keras
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization
intro: ECCV 2016
project page: http://www.di.ens.fr/willow/research/contextlocnet/
arxiv: http://arxiv.org/abs/1609.04331
github: https://github.com/vadimkantorov/contextlocnet
Ensemble of Part Detectors for Simultaneous Classification and Localization
arxiv: https://arxiv.org/abs/1705.10034
STNet: Selective Tuning of Convolutional Networks for Object Localization
arxiv: https://arxiv.org/abs/1708.06418
Soft Proposal Networks for Weakly Supervised Object Localization
intro: ICCV 2017
arxiv: https://arxiv.org/abs/1709.01829
Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN
intro: ACM MM 2017
arxiv: https://arxiv.org/abs/1709.08295
Tutorials / Talks
Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection
slides: http://research.microsoft.com/en-us/um/people/kahe/iccv15tutorial/iccv2015_tutorial_convolutional_feature_maps_kaiminghe.pdf
Towards Good Practices for Recognition & Detection
intro: Hikvision Research Institute. Supervised Data Augmentation (SDA)
slides: http://image-net.org/challenges/talks/2016/Hikvision_at_ImageNet_2016.pdf
Work in progress: Improving object detection and instance segmentation for small objects
https://docs.google.com/presentation/d/1OTfGn6mLe1VWE8D0q6Tu_WwFTSoLGd4OF8WCYnOWcVo/edit
Object Detection with Deep Learning: A Review
arxiv: https://arxiv.org/abs/1807.05511
Projects
Detectron
intro: FAIR’s research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
github: https://github.com/facebookresearch/Detectron
TensorBox: a simple framework for training neural networks to detect objects in images
intro: “The basic model implements the simple and robust GoogLeNet-OverFeat algorithm. We additionally provide an implementation of the ReInspect algorithm”
github: https://github.com/Russell91/TensorBox
Object detection in torch: Implementation of some object detection frameworks in torch
github: https://github.com/fmassa/object-detection.torch
Using DIGITS to train an Object Detection network
github: https://github.com/NVIDIA/DIGITS/blob/master/examples/object-detection/README.md
FCN-MultiBox Detector
intro: Full convolution MultiBox Detector (like SSD) implemented in Torch.
github: https://github.com/teaonly/FMD.torch
KittiBox: A car detection model implemented in Tensorflow.
keywords: MultiNet
intro: KittiBox is a collection of scripts to train out model FastBox on the Kitti Object Detection Dataset
github: https://github.com/MarvinTeichmann/KittiBox
Deformable Convolutional Networks + MST + Soft-NMS
github: https://github.com/bharatsingh430/Deformable-ConvNets
How to Build a Real-time Hand-Detector using Neural Networks (SSD) on Tensorflow
blog: https://towardsdatascience.com/how-to-build-a-real-time-hand-detector-using-neural-networks-ssd-on-tensorflow-d6bac0e4b2ce
github: https://github.com//victordibia/handtracking
Metrics for object detection
intro: Most popular metrics used to evaluate object detection algorithms
github: https://github.com/rafaelpadilla/Object-Detection-Metrics
MobileNetv2-SSDLite
intro: Caffe implementation of SSD and SSDLite detection on MobileNetv2, converted from tensorflow.
github: https://github.com/chuanqi305/MobileNetv2-SSDLite
Leaderboard
Detection Results: VOC2012
intro: Competition “comp4” (train on additional data)
homepage: http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4
Tools
BeaverDam: Video annotation tool for deep learning training labels
https://github.com/antingshen/BeaverDam
Blogs
Convolutional Neural Networks for Object Detection
http://rnd.azoft.com/convolutional-neural-networks-object-detection/
Introducing automatic object detection to visual search (Pinterest)
keywords: Faster R-CNN
blog: https://engineering.pinterest.com/blog/introducing-automatic-object-detection-visual-search
demo: https://engineering.pinterest.com/sites/engineering/files/Visual Search V1 - Video.mp4
review: https://news.developer.nvidia.com/pinterest-introduces-the-future-of-visual-search/?mkt_tok=eyJpIjoiTnpaa01UWXpPRE0xTURFMiIsInQiOiJJRjcybjkwTmtmallORUhLOFFFODBDclFqUlB3SWlRVXJXb1MrQ013TDRIMGxLQWlBczFIeWg0TFRUdnN2UHY2ZWFiXC9QQVwvQzBHM3B0UzBZblpOSmUyU1FcLzNPWXI4cml2VERwTTJsOFwvOEk9In0%3D
Deep Learning for Object Detection with DIGITS
blog: https://devblogs.nvidia.com/parallelforall/deep-learning-object-detection-digits/
Analyzing The Papers Behind Facebook’s Computer Vision Approach
keywords: DeepMask, SharpMask, MultiPathNet
blog: https://adeshpande3.github.io/adeshpande3.github.io/Analyzing-the-Papers-Behind-Facebook’s-Computer-Vision-Approach/
Easily Create High Quality Object Detectors with Deep Learning
intro: dlib v19.2
blog: http://blog.dlib.net/2016/10/easily-create-high-quality-object.html
How to Train a Deep-Learned Object Detection Model in the Microsoft Cognitive Toolkit
blog: https://blogs.technet.microsoft.com/machinelearning/2016/10/25/how-to-train-a-deep-learned-object-detection-model-in-cntk/
github: https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Detection/FastRCNN
Object Detection in Satellite Imagery, a Low Overhead Approach
part 1: https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-i-cbd96154a1b7
part 2: https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-ii-893f40122f92
You Only Look Twice?—?Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural Networks
part 1: https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-38dad1cf7571
part 2: https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-34f72f659588
Faster R-CNN Pedestrian and Car Detection
blog: https://bigsnarf.wordpress.com/2016/11/07/faster-r-cnn-pedestrian-and-car-detection/
ipn: https://gist.github.com/bigsnarfdude/2f7b2144065f6056892a98495644d3e0
github: https://github.com/bigsnarfdude/Faster-RCNN_TF
Small U-Net for vehicle detection
blog: https://medium.com/@vivek.yadav/small-u-net-for-vehicle-detection-9eec216f9fd6
Region of interest pooling explained
blog: https://deepsense.io/region-of-interest-pooling-explained/
github: https://github.com/deepsense-io/roi-pooling
Supercharge your Computer Vision models with the TensorFlow Object Detection API
blog: https://research.googleblog.com/2017/06/supercharge-your-computer-vision-models.html
github: https://github.com/tensorflow/models/tree/master/object_detection
Understanding SSD MultiBox?—?Real-Time Object Detection In Deep Learning
https://towardsdatascience.com/understanding-ssd-multibox-real-time-object-detection-in-deep-learning-495ef744fab
One-shot object detection
http://machinethink.net/blog/object-detection/
An overview of object detection: one-stage methods
https://www.jeremyjordan.me/object-detection-one-stage/
deep learning object detection
intro: A paper list of object detection using deep learning.
github: https://github.com/hoya012/deep_learning_object_detection
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