Decision-level information fusion powered human pose estimation

Human pose estimation is viewed as a crucial step for understanding human behaviour. Although significant progress has been made in this area in recent years, most studies have focused on feature-level information fusion, while decision-level information fusion has rarely been explored. Compared with feature-level information, decision-level information contains more semantic and interpretable information and can help improve the performance of pose estimation in occluded and crowded scenes. In this paper, we focus on the fusion of decision-level information. We propose a View Fusion module for aggregating decision-level information from different stages to generate a more comprehensive estimation. An Auxiliary Task module is introduced to bridge the gap between the feature extractor and the View Fusion module and to provide prior information about the form of the decision-level information. Considering that the precision of predictions from different stages varies, we use different strategies to guide the learning process. Experiments show that our models outperform previous methods and achieve competitive results on the CrowdPose test set. Further experiments indicate that our method is flexible and can improve the performance of various backbones.