DL之Panoptic Segmentation：Panoptic Segmentation(全景分割)的簡(jiǎn)介(論文介紹)、全景分割挑戰(zhàn)簡(jiǎn)介、案例應(yīng)用等配圖集合之詳細(xì)攻略

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目錄

Panoptic Segmentation(全景分割)的簡(jiǎn)介(論文介紹)

0、論文簡(jiǎn)介

Panoptic Segmentation全景分割挑戰(zhàn)簡(jiǎn)介

Panoptic Segmentation(全景分割)的案例應(yīng)用

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相關(guān)文章
DL之Panoptic Segmentation：Panoptic Segmentation(全景分割)的簡(jiǎn)介(論文介紹)、全景分割挑戰(zhàn)簡(jiǎn)介、案例應(yīng)用等配圖集合之詳細(xì)攻略
DL之Panoptic Segmentation：Panoptic Segmentation(全景分割)的全景分割挑戰(zhàn)的簡(jiǎn)介

Panoptic Segmentation(全景分割)的簡(jiǎn)介(論文介紹)

? ? ?本論文源自FaceBook的研究人員。

Abstract ?
? ? ? ?We propose and study a task we name panoptic segmentation ?(PS). Panoptic segmentation unifies the typically distinct ?tasks of semantic segmentation (assign a class label to ?each pixel) and instance segmentation (detect and segment ?each object instance). The proposed task requires generating ?a coherent scene segmentation that is rich and complete, ?an important step toward real-world vision systems. ?While early work in computer vision addressed related image/scene ?parsing tasks, these are not currently popular, ?possibly due to lack of appropriate metrics or associated ?recognition challenges. To address this, we propose a novel ?panoptic quality (PQ) metric that captures performance for ?all classes (stuff and things) in an interpretable and unified ?manner. Using the proposed metric, we perform a rigorous ?study of both human and machine performance for PS on ?three existing datasets, revealing interesting insights about ?the task. The aim of our work is to revive the interest of the ?community in a more unified view of image segmentation.dual stuff-and-thing nature of PS. A number of instance segmentation ?approaches including [28, 2, 3, 18] are designed ?to produce non-overlapping instance predictions and could ?serve as the foundation of such a system. (2) Since a PS ?cannot have overlapping segments, some form of higherlevel ?‘reasoning’ may be beneficial, for example, based on ?extending learnable NMS [7, 16] to PS. We hope that the ?panoptic segmentation task will invigorate research in these ?areas leading to exciting new breakthroughs in vision. ?Finally we note that the panoptic segmentation task was ?featured as a challenge track by both the COCO [25] and ?Mapillary Vistas [35] recognition challenges and that the ?proposed task has already begun to gain traction in the community ?(e.g. [23, 48, 49, 27, 22, 21, 17] address PS).
? ? ? ?我們提出并研究了一項(xiàng)稱為全景分割(PS)的任務(wù)。泛光分割統(tǒng)一了語義分割(為每個(gè)像素分配一個(gè)類標(biāo)簽)和實(shí)例分割(檢測(cè)和分割每個(gè)對(duì)象實(shí)例)這兩個(gè)典型的不同任務(wù)。提出的任務(wù)需要生成一個(gè)連貫的場(chǎng)景分割是豐富和完整的，一個(gè)重要的步驟，向現(xiàn)實(shí)世界的視覺系統(tǒng)。雖然早期的計(jì)算機(jī)視覺工作解決了相關(guān)的圖像/場(chǎng)景解析任務(wù)，但這些任務(wù)目前并不流行，這可能是因?yàn)槿狈m當(dāng)?shù)亩攘繕?biāo)準(zhǔn)或相關(guān)的識(shí)別挑戰(zhàn)。為了解決這個(gè)問題，我們提出了一種新的全景質(zhì)量(PQ)矩陣，它以一種可解釋和統(tǒng)一的方式捕獲所有類(東西和東西)的性能。使用提議的度量，我們對(duì)現(xiàn)有的三個(gè)數(shù)據(jù)集上的PS的人和機(jī)器性能進(jìn)行了嚴(yán)格的研究，揭示了關(guān)于該任務(wù)的有趣見解。我們的工作目標(biāo)是喚起社會(huì)各界對(duì)圖像分割的興趣，以更統(tǒng)一的視角進(jìn)行圖像分割。許多實(shí)例分割方法，包括[28,2,3,18]，旨在產(chǎn)生非重疊的實(shí)例預(yù)測(cè)，并可作為這樣一個(gè)系統(tǒng)的基礎(chǔ)。(2)由于一個(gè)PS不能有重疊的片段，某種形式的高層次的“推理”可能是有益的，例如，基于將可學(xué)習(xí)的NMS[7,16]擴(kuò)展到PS，我們希望全景分割任務(wù)能夠活躍這些領(lǐng)域的研究，從而在視覺方面帶來令人興奮的新突破。最后，我們注意到，COCO[25]和map腋下遠(yuǎn)景[35]識(shí)別挑戰(zhàn)都將全光分割任務(wù)作為一個(gè)挑戰(zhàn)軌跡，并且所提出的任務(wù)已經(jīng)開始在社區(qū)中獲得關(guān)注(例如，[23, 48, 49, 27, 22, 21, 17]地址PS)。
Future of Panoptic Segmentation ?
? ? ? ?Our goal is to drive research in novel directions by inviting ?the community to explore the new panoptic segmentation ?task. We believe that the proposed task can lead to ?expected and unexpected innovations. We conclude by discussing ?some of these possibilities and our future plans. ?
? ? ? ?我們的目標(biāo)是通過邀請(qǐng)社區(qū)來探索新的全景分割任務(wù)，從而將研究推向新的方向。我們認(rèn)為，擬議的任務(wù)可以導(dǎo)致預(yù)期的和意想不到的創(chuàng)新。最后，我們討論了其中一些可能性和我們未來的計(jì)劃。
? ? ? ?Motivated by simplicity, the PS ‘a(chǎn)lgorithm’ in this paper ?is based on the heuristic combination of outputs from topperforming ?instance and semantic segmentation systems. ?This approach is a basic first step, but we expect more interesting ?algorithms to be introduced. Specifically, we hope to ?see PS drive innovation in at least two areas: (1) Deeply integrated ?end-to-end models that simultaneously address the dual stuff-and-thing nature of PS. A number of instance segmentation ?approaches including [28, 2, 3, 18] are designed ?to produce non-overlapping instance predictions and could ?serve as the foundation of such a system. (2) Since a PS ?cannot have overlapping segments, some form of higherlevel ?‘reasoning’ may be beneficial, for example, based on ?extending learnable NMS [7, 16] to PS. We hope that the ?panoptic segmentation task will invigorate research in these ?areas leading to exciting new breakthroughs in vision. ?
? ? ? ?本論文的PS“算法”以簡(jiǎn)單為動(dòng)機(jī)，基于top performance實(shí)例輸出和語義分割系統(tǒng)的啟發(fā)式組合。這種方法是基本的第一步，但我們希望引入更多有趣的算法。具體地說,我們希望看到PS驅(qū)動(dòng)創(chuàng)新至少在兩個(gè)方面:(1)深入集成的端到端模型,同時(shí)解決雙重stuff-and-thing PS的性質(zhì)。許多實(shí)例分割方法包括(28日,2、3、18)是用來產(chǎn)生重疊實(shí)例預(yù)測(cè),可以作為這樣的一個(gè)系統(tǒng)的基礎(chǔ)。(2)由于一個(gè) PS不能有重疊的片段，某種形式的高層次的“推理”可能是有益的，例如，基于將可學(xué)習(xí)的NMS[7,16]擴(kuò)展到PS，我們希望泛光分割任務(wù)能夠活躍這些領(lǐng)域的研究，從而在視覺方面帶來令人興奮的新突破。
? ? ? ?Finally we note that the panoptic segmentation task was ?featured as a challenge track by both the COCO [25] and ?Mapillary Vistas [35] recognition challenges and that the ?proposed task has already begun to gain traction in the community ?(e.g. [23, 48, 49, 27, 22, 21, 17] address PS).
? ? ? ?最后，我們注意到，COCO[25]和Mapillary Vistas [35]識(shí)別挑戰(zhàn)都將全景分割任務(wù)作為一個(gè)挑戰(zhàn)軌跡，并且所提出的任務(wù)已經(jīng)開始在社區(qū)中獲得關(guān)注(例如，[23, 48, 49, 27, 22, 21, 17]地址PS)。

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論文
Alexander Kirillov, KaimingHe, Ross Girshick, Carsten Rother, Piotr Dollár.
Panoptic Segmentation
https://arxiv.org/pdf/1801.00868.pdf

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0、論文簡(jiǎn)介

CV之IS：計(jì)算機(jī)視覺之圖像分割(Image Segmentation)算法的挑戰(zhàn)任務(wù)、算法演化、目標(biāo)檢測(cè)和圖像分割的對(duì)比

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Panoptic Segmentation全景分割挑戰(zhàn)簡(jiǎn)介

DL之Panoptic Segmentation：Panoptic Segmentation(全景分割)的全景分割挑戰(zhàn)的簡(jiǎn)介

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Panoptic Segmentation(全景分割)的案例應(yīng)用

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