Fast Adaptation for Human Pose Estimation via Meta-Optimization
Authors: Shengxiang Hu, Huaijiang Sun, Bin Li, Dong Wei, Weiqing Li, Jianfeng Lu

Domain shift is a challenge for supervised human pose estimation where the source data and target data come from different distributions. This is why pose estimation methods generally perform worse on the test set than on the training set. Recently test-time adaptation has proven to be an effective way to deal with domain shift in human pose estimation. Although the performance on the target domain has been improved existing methods require a large number of weight updates for convergence which is time-consuming and brings catastrophic forgetting. To solve these issues we propose a meta-auxiliary learning method to achieve fast adaptation for domain shift during inference. Specifically we take human pose estimation as the supervised primary task and propose body-specific image inpainting as a self-supervised auxiliary task. First we jointly train the primary and auxiliary tasks to get a pre-trained model on the source domain. Then meta-training correlates the performance of the two tasks to learn a good weight initialization. Finally meta-testing adapts the meta-learned model to the target data through self-supervised learning. Benefiting from the meta-learning paradigm the proposed method enables fast adaptation to the target domain while preserving the source domain knowledge. The carefully designed auxiliary task better pays attention to human-related semantics in a single image. Extensive experiments demonstrate the effectiveness of our test-time fast adaptation.