Summary of Escape: Energy-based Selective Adaptive Correction For Out-of-distribution 3d Human Pose Estimation, by Luke Bidulka et al.
ESCAPE: Energy-based Selective Adaptive Correction for Out-of-distribution 3D Human Pose Estimation
by Luke Bidulka, Mohsen Gholami, Jiannan Zheng, Martin J. McKeown, Z. Jane Wang
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes ESCAPE, a lightweight framework for improving human pose estimation (HPE) performance in out-of-distribution (OOD) scenarios. The authors observe that not all test-time samples are OOD and that errors are larger on distal keypoints like the wrist and ankle. They introduce a free energy function to separate OOD samples from incoming data, a correction network to estimate HPE prediction errors on distal keypoints, and a self-consistency adaptation loss to update the correction network by leveraging the constraining relationship between distal and proximal keypoints. ESCAPE improves the distal MPJPE of five popular HPE models by up to 7% on unseen data and achieves state-of-the-art results on two popular HPE benchmarks while being significantly faster than existing adaptation methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines get better at recognizing people’s poses, even when they’re not in a familiar setting. The problem is that computers can be really bad at this when the person or pose is new to them. To fix this, the authors created a way to correct and adapt their computer vision model so it can do better on new data. They tested it on different people and poses and found that it works really well, even beating other popular methods in some cases. |
Keywords
» Artificial intelligence » Pose estimation
