Improved TokenPose with Sparsity
Anning Li
Over the past few years, the vision transformer and its various forms have
gained significance in human pose estimation. By treating image patches as
tokens, transformers can capture global relationships wisely, estimate the
keypoint tokens by leveraging the visual tokens, and recognize the posture of
the human body. Nevertheless, global attention is computationally demanding,
which poses a challenge for scaling up transformer-based methods to
high-resolution features. In this paper, we introduce sparsity in both keypoint
token attention and visual token attention to improve human pose estimation.
Experimental results on the MPII dataset demonstrate that our model has a
higher level of accuracy and proved the feasibility of the method, achieving
new state-of-the-art results. The idea can also provide references for other
transformer-based models.