DL之Panoptic Segmentation：Panoptic Segmentation(全景分割)的簡介(論文介紹)、全景分割挑戰簡介、案例應用等配圖集合之詳細攻略

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目錄

Panoptic Segmentation(全景分割)的簡介(論文介紹)

0、論文簡介

Panoptic Segmentation全景分割挑戰簡介

Panoptic Segmentation(全景分割)的案例應用

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相關文章
DL之Panoptic Segmentation：Panoptic Segmentation(全景分割)的簡介(論文介紹)、全景分割挑戰簡介、案例應用等配圖集合之詳細攻略
DL之Panoptic Segmentation：Panoptic Segmentation(全景分割)的全景分割挑戰的簡介

Panoptic Segmentation(全景分割)的簡介(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稱為全景分割(PS)的任務。泛光分割統一了語義分割(為每個像素分配一個類標簽)和實例分割(檢測和分割每個對象實例)這兩個典型的不同任務。提出的任務需要生成一個連貫的場景分割是豐富和完整的，一個重要的步驟，向現實世界的視覺系統。雖然早期的計算機視覺工作解決了相關的圖像/場景解析任務，但這些任務目前并不流行，這可能是因為缺乏適當的度量標準或相關的識別挑戰。為了解決這個問題，我們提出了一種新的全景質量(PQ)矩陣，它以一種可解釋和統一的方式捕獲所有類(東西和東西)的性能。使用提議的度量，我們對現有的三個數據集上的PS的人和機器性能進行了嚴格的研究，揭示了關于該任務的有趣見解。我們的工作目標是喚起社會各界對圖像分割的興趣，以更統一的視角進行圖像分割。許多實例分割方法，包括[28,2,3,18]，旨在產生非重疊的實例預測，并可作為這樣一個系統的基礎。(2)由于一個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，基于將可學習的NMS[7,16]擴展到PS，我們希望全景分割任務能夠活躍這些領域的研究，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腋下遠景[35]識別挑戰都將全光分割任務作為一個挑戰軌跡，并且所提出的任務已經開始在社區中獲得關注(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. ?
? ? ? ?我們的目標是通過邀請社區來探索新的全景分割任務，從而將研究推向新的方向。我們認為，擬議的任務可以導致預期的和意想不到的創新。最后，我們討論了其中一些可能性和我們未來的計劃。
? ? ? ?Motivated by simplicity, the PS ‘algorithm’ 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”以簡單為動機，基于top performance實例輸出和語義分割系統的啟發式組合。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驅動創新至少在兩個方面:(1)深入集成的端到端模型,同時解決雙重stuff-and-thing PS的性質。許多實例分割方法包括(28日,2、3、18)是用來產生重疊實例預測,可以作為這樣的一個系統的基礎。(2)由于一個 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，基于將可學習的NMS[7,16]擴展到PS，我們希望泛光分割任務能夠活躍這些領域的研究，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]識別挑戰都將全景分割任務作為一個挑戰軌跡，并且所提出的任務已經開始在社區中獲得關注(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、論文簡介

CV之IS：計算機視覺之圖像分割(Image Segmentation)算法的挑戰任務、算法演化、目標檢測和圖像分割的對比

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Panoptic Segmentation全景分割挑戰簡介

DL之Panoptic Segmentation：Panoptic Segmentation(全景分割)的全景分割挑戰的簡介

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Panoptic Segmentation(全景分割)的案例應用

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