CVPR 2023 - Self-Correctable and Adaptable Inference for Generalizable Human Pose Estimation

In this episode we discuss Self-Correctable and Adaptable Inference for Generalizable Human Pose Estimation by Zhehan Kan, Shuoshuo Chen, Ce Zhang, Yushun Tang, Zhihai He. The paper introduces a self-correctable and adaptable inference (SCAI) method to address the generalization challenge of network prediction. Utilizing human pose estimation as an example, they learn a correction network to correct the prediction result conditioned by a fitness feedback error. This feedback error is generated by a learned fitness feedback network that maps the prediction result to the original input domain and compares it against the original input, which can be used as feedback to guide the correction process and as a loss function to optimize the correction network during the inference process. Experimental results demonstrate that the proposed SCAI method significantly improves the generalization capability and performance of human pose estimation.