Summary of Source-free and Image-only Unsupervised Domain Adaptation For Category Level Object Pose Estimation, by Prakhar Kaushik et al.
Source-Free and Image-Only Unsupervised Domain Adaptation for Category Level Object Pose Estimation
by Prakhar Kaushik, Aayush Mishra, Adam Kortylewski, Alan Yuille
First submitted to arxiv on: 19 Jan 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 tackles the challenging problem of adapting source-free unsupervised category-level pose estimation models to new RGB image domains without access to 3D data or annotations. The authors introduce 3DUDA, a method that can adapt to these nuisance-ridden target domains by leveraging invariant object subparts. They represent object categories as simple cuboid meshes and use generative models of neural feature activations learned using differential rendering. By focusing on individual mesh vertex features and iteratively updating them based on proximity to corresponding features in the target domain, 3DUDA simulates fine-tuning on a global pseudo-labeled dataset under mild assumptions, which converges to the target domain asymptotically. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how machines can learn from new images without needing extra information. The authors created a special method called 3DUDA that can adapt to new image domains by finding stable parts of objects. They use simple shapes and clever algorithms to do this. By updating these object parts based on the new images, 3DUDA can improve pose estimation accuracy even when there’s noise or occlusion. |
Keywords
» Artificial intelligence » Fine tuning » Pose estimation » Unsupervised
