@inproceedings{Regularizing_Vector_Embedding,title={Regularizing Vector Embedding in Bottom-Up Human Pose Estimation},author={},year={2022},tags={human-pose-estimation, bottom-up},booktitle={ECCV},}
In keypoint estimation tasks such as human pose estimation, heatmap-basedregression is the dominant approach despite possessing notable drawbacks:heatmaps intrinsically suffer from quantization error and require excessivecomputation to generate and post-process. Motivated to find a more efficientsolution, we propose to model individual keypoints and sets of spatiallyrelated keypoints (i.e., poses) as objects within a dense single-stageanchor-based detection framework. Hence, we call our method KAPAO (pronounced"Ka-Pow"), for Keypoints And Poses As Objects. KAPAO is applied to the problemof single-stage multi-person human pose estimation by simultaneously detectinghuman pose and keypoint objects and fusing the detections to exploit thestrengths of both object representations. In experiments, we observe that KAPAOis faster and more accurate than previous methods, which suffer greatly fromheatmap post-processing. The accuracy-speed trade-off is especially favourablein the practical setting when not using test-time augmentation. Source code:https://github.com/wmcnally/kapao.
把关键点当做物体来直接用yolo回归
@inproceedings{KAPAO,title={Rethinking Keypoint Representations: Modeling Keypoints and Poses as Objects for Multi-Person Human Pose Estimation},author={McNally, William and Vats, Kanav and Wong, Alexander and McPhee, John},year={2021},tags={2dpose, bottom-up},booktitle={ECCV},}
Regularizing Vector Embedding in Bottom-Up Human Pose EstimationIn ECCV 2022human-pose-estimation bottom-up