| # Convolutional pose machines | |
| <!-- [ALGORITHM] --> | |
| <details> | |
| <summary align="right"><a href="http://openaccess.thecvf.com/content_cvpr_2016/html/Wei_Convolutional_Pose_Machines_CVPR_2016_paper.html">CPM (CVPR'2016)</a></summary> | |
| ```bibtex | |
| @inproceedings{wei2016convolutional, | |
| title={Convolutional pose machines}, | |
| author={Wei, Shih-En and Ramakrishna, Varun and Kanade, Takeo and Sheikh, Yaser}, | |
| booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition}, | |
| pages={4724--4732}, | |
| year={2016} | |
| } | |
| ``` | |
| </details> | |
| ## Abstract | |
| <!-- [ABSTRACT] --> | |
| We introduce associative embedding, a novel method for supervising convolutional neural networks for the task of detection and grouping. A number of computer vision problems can be framed in this manner including multi-person pose estimation, instance segmentation, and multi-object tracking. Usually the grouping of detections is achieved with multi-stage pipelines, instead we propose an approach that teaches a network to simultaneously output detections and group assignments. This technique can be easily integrated into any state-of-the-art network architecture that produces pixel-wise predictions. We show how to apply this method to both multi-person pose estimation and instance segmentation and report state-of-the-art performance for multi-person pose on the MPII and MS-COCO datasets. | |
| <!-- [IMAGE] --> | |
| <div align=center> | |
| <img src="https://user-images.githubusercontent.com/15977946/146514331-a599580b-69a5-4ee4-9aaf-4a72f9c25c9a.png"> | |
| </div> | |