Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing

• URL: http://arxiv.org/abs/2103.04570v1
• Date: Mon, 8 Mar 2021 06:55:00 GMT
• Title: Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing
• Authors: Tianfei Zhou, Wenguan Wang, Si Liu, Yi Yang, Luc Van Gool
• Abstract summary: A new bottom-up regime is proposed to learn category-level human semantic segmentation and multi-person pose estimation in a joint and end-to-end manner. It is a compact, efficient and powerful framework that exploits structural information over different human granularities. Experiments on three instance-aware human datasets show that our model outperforms other bottom-up alternatives with much more efficient inference.
• Score: 131.97475877877608
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To address the challenging task of instance-aware human part parsing, a new bottom-up regime is proposed to learn category-level human semantic segmentation as well as multi-person pose estimation in a joint and end-to-end manner. It is a compact, efficient and powerful framework that exploits structural information over different human granularities and eases the difficulty of person partitioning. Specifically, a dense-to-sparse projection field, which allows explicitly associating dense human semantics with sparse keypoints, is learnt and progressively improved over the network feature pyramid for robustness. Then, the difficult pixel grouping problem is cast as an easier, multi-person joint assembling task. By formulating joint association as maximum-weight bipartite matching, a differentiable solution is developed to exploit projected gradient descent and Dykstra's cyclic projection algorithm. This makes our method end-to-end trainable and allows back-propagating the grouping error to directly supervise multi-granularity human representation learning. This is distinguished from current bottom-up human parsers or pose estimators which require sophisticated post-processing or heuristic greedy algorithms. Experiments on three instance-aware human parsing datasets show that our model outperforms other bottom-up alternatives with much more efficient inference.

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