Pointly-Supervised Panoptic Segmentation
- URL: http://arxiv.org/abs/2210.13950v1
- Date: Tue, 25 Oct 2022 12:03:51 GMT
- Title: Pointly-Supervised Panoptic Segmentation
- Authors: Junsong Fan, Zhaoxiang Zhang, Tieniu Tan
- Abstract summary: 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.
We formulate the problem in an end-to-end framework by simultaneously generating panoptic pseudo-masks from point-level labels and learning from them.
- Score: 106.68888377104886
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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