点监督全视分割

Junsong Fan, Zhaoxiang Zhang, T. Tan
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引用次数: 10

摘要

本文提出了一种将点级标注应用于弱监督全视分割的新方法。与全监督方法使用的密集像素级标签不同,点级标签只为每个目标提供一个点作为监督,大大减少了标注负担。我们通过从点级标签同时生成全景伪掩模并从中学习,在端到端框架中制定问题。为了解决核心挑战,即泛光伪掩码生成,我们提出了一种原则性的方法,通过最小化像素到点的遍历成本来解析像素,该方法建模语义相似性,低级纹理线索和高级流形知识来区分泛光目标。我们在Pascal VOC和MS COCO数据集上进行了实验,以证明该方法的有效性,并在弱监督全光分割问题中展示了最先进的性能。代码可在https://github.com/BraveGroup/PSPS.git上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pointly-Supervised Panoptic Segmentation
In this paper, we propose a new approach to applying point-level annotations for weakly-supervised panoptic segmentation. Instead of the dense pixel-level labels used by fully supervised methods, point-level labels only provide a single point for each target as supervision, significantly reducing the annotation burden. We formulate the problem in an end-to-end framework by simultaneously generating panoptic pseudo-masks from point-level labels and learning from them. To tackle the core challenge, i.e., panoptic pseudo-mask generation, we propose a principled approach to parsing pixels by minimizing pixel-to-point traversing costs, which model semantic similarity, low-level texture cues, and high-level manifold knowledge to discriminate panoptic targets. We conduct experiments on the Pascal VOC and the MS COCO datasets to demonstrate the approach's effectiveness and show state-of-the-art performance in the weakly-supervised panoptic segmentation problem. Codes are available at https://github.com/BraveGroup/PSPS.git.
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