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PoSeg: Pose-Aware Refinement Network for Human Instance Segmentation
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DOI：10.1109/ACCESS.2020.2967147.png)
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Abstract
En 中文
Human instance segmentation is a core problem for human-centric scene understanding and segmenting human instances poses a unique challenge to vision systems due to large intra-class variations in both appearance and shape, and complicated occlusion patterns. In this paper, we propose a new pose-aware human instance segmentation method. Compared to the previous pose-aware methods which first predict bottom-up poses and then estimate instance segmentation on top of predicted poses, our method integrates both top-down and bottom-up cues for an instance: it adopts detection results as human proposals and jointly estimates human pose and instance segmentation for each proposal. We develop a modular recurrent deep network that utilizes pose estimation to refine instance segmentation in an iterative manner. Our refinement modules exploit pose cues in two levels: as a coarse shape prior and local part attention. We evaluate our approach on two public multi-person benchmarks: OCHuman dataset and COCOPersons dataset. The proposed method surpasses the state-of-the-art methods on OCHuman dataset by 3.0 mAP and on COCOPersons by 6.4 mAP, demonstrating the effectiveness of our approach.
Keywords:
Detection
human instance segmentation
pose estimation
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