Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation

Liu, Yongtuo; Xu, Dan; Ren, Sucheng; Wu, Huawei; Cai, Hongmin; He, Shengfeng

doi:10.48550/arxiv.2108.02980

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Point-level Labels In Visual Tasks1

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2022

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“…Recently point-level annotation has drawn interest in a broad range of computer vision tasks. Beside the works concerning the detection and segmentation tasks [7,29,38,2], some works adopt point-level labels to train crowd counting [50,33] models. SPTS [37] proposes to use points for the text spotting problem.…”

Section: Point-level Labels In Visual Tasksmentioning

confidence: 99%

Pointly-Supervised Panoptic Segmentation

Fan

Zhang

Tan

2022

Lecture Notes in Computer Science

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In this paper, we propose a new approach to applying pointlevel annotations for weakly-supervised panoptic segmentation. Instead of the dense pixel-level labels used by fully supervised methods, pointlevel 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 pseudomasks 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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