Pose Recognition with Cascade Transformers

• URL: http://arxiv.org/abs/2104.06976v1
• Date: Wed, 14 Apr 2021 17:00:22 GMT
• Title: Pose Recognition with Cascade Transformers
• Authors: Ke Li, Shijie Wang, Xiang Zhang, Yifan Xu, Weijian Xu, Zhuowen Tu
• Abstract summary: We present a regression-based pose recognition method using Transformers. Heatmap-based and regression-based methods achieve higher accuracy but are subject to various designs. In the experiments, we report competitive results for pose recognition when compared with the competing regression-based methods.
• Score: 31.7059023190426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present a regression-based pose recognition method using cascade Transformers. One way to categorize the existing approaches in this domain is to separate them into 1). heatmap-based and 2). regression-based. In general, heatmap-based methods achieve higher accuracy but are subject to various heuristic designs (not end-to-end mostly), whereas regression-based approaches attain relatively lower accuracy but they have less intermediate non-differentiable steps. Here we utilize the encoder-decoder structure in Transformers to perform regression-based person and keypoint detection that is general-purpose and requires less heuristic design compared with the existing approaches. We demonstrate the keypoint hypothesis (query) refinement process across different self-attention layers to reveal the recursive self-attention mechanism in Transformers. In the experiments, we report competitive results for pose recognition when compared with the competing regression-based methods.

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