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arXiv · 2210.13950

Pointly-Supervised Panoptic Segmentation

Junsong Fan·Zhaoxiang Zhang·Tieniu Tan
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Abstract

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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Junsong Fan, Zhaoxiang Zhang, Tieniu Tan. 2022-10-25. Pointly-Supervised Panoptic Segmentation. https://arxiv.org/abs/2210.13950

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