  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.
