PoseImageNet: Pose Estimation for Extensive Classes Based on Rich Structure Prototypes

It is intuitive for human vision system to perceive the objectposition, area and structure, which corresponds to the tasks of detection,segmentation, and pose estimation. Compared to the modern detectorsor segmentors, existing pose estimators only cover specific classes (e.g.,human body) and restricted structures. Thus, recent works gather previ-ous single-class pose datasets to construct multi-class pose datasets, butthe richness of classes and structures are still limited. To this end, we la-bel a new pose dataset upon ImageNet dataset, which contains extensiveclasses and structures. Due to the structure diversity in single class, wedecompose into two sub-tasks based on structure prototypes: prototypelabeling and pose annotating. In this way, we obtain 3500+ structureprototypes of 720 semantic classes. Although different prototypes mayhave diverse structures, there are some shared keypoints. Therefore, wepropose an appropriate baseline with query-based model and matching-based supervision, where a query will learn the shared information acrosskeypoints and focus on estimating the keypoints with similar character-istics. Extensive analyses and experiments indicate the quality of ourdataset and the effectiveness of our method.