Bias-Compensated Integral Regression for Human Pose Estimation.

Area of Science:

• Computer Vision
• Machine Learning
• Pose Estimation

Background:

• Heatmaps are vital for human and hand pose estimation, representing keypoint locations.
• Current methods decode heatmaps using argmax (detection) or softmax/expectation (integral regression).
• Integral regression offers end-to-end learning but suffers from lower accuracy compared to detection.

Purpose of the Study:

• To uncover and address the bias in integral regression for pose estimation.
• To improve the accuracy and training efficiency of heatmap-based pose estimation methods.
• To develop a novel framework competitive with state-of-the-art detection techniques.

Main Methods:

• Investigated the induced bias in integral regression from combining softmax and expectation operations.
• Analyzed gradients to understand training convergence issues in integral regression.
• Proposed Bias Compensated Integral Regression (BCIR) to mitigate bias and incorporated a Gaussian prior loss.

Main Results:

• Identified that integral regression bias forces degenerate heatmap learning, obscuring keypoint distributions.
• Demonstrated that integral regression has slower heatmap update convergence compared to detection.
• BCIR framework showed faster training and improved accuracy on human and hand pose benchmarks.

Conclusions:

• The proposed BCIR framework effectively compensates for integral regression bias.
• BCIR achieves competitive accuracy and training speed compared to state-of-the-art detection methods.
• BCIR offers a more robust and efficient approach to heatmap decoding in pose estimation.