Summary of Featup: a Model-agnostic Framework For Features at Any Resolution, by Stephanie Fu et al.
FeatUp: A Model-Agnostic Framework for Features at Any Resolution
by Stephanie Fu, Mark Hamilton, Laura Brandt, Axel Feldman, Zhoutong Zhang, William T. Freeman
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 Deep features are a crucial component of computer vision research, enabling the solution of various tasks even with zero or few-shot training. However, these features often lack spatial resolution, making them unsuitable for dense prediction tasks like segmentation and depth estimation due to aggressive pooling. To address this limitation, we introduce FeatUp, a task-agnostic framework that restores lost spatial information in deep features. Our framework consists of two variants: one using high-resolution signal and another fitting an implicit model to reconstruct features at any resolution. Both approaches employ multi-view consistency loss with deep analogies to NeRFs. Our features retain their original semantics and can be seamlessly integrated into existing applications without re-training, leading to performance gains. In experiments, we demonstrate that FeatUp outperforms other feature upsampling and image super-resolution methods in tasks like class activation map generation, transfer learning for segmentation and depth prediction, and end-to-end training for semantic segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FeatUp is a new way to make computer vision models better at understanding what’s happening in images. Right now, these models can only see broad things, not details. FeatUp helps by adding more detail to the model’s understanding of an image, making it better at tasks like recognizing objects and predicting depth. |
Keywords
* Artificial intelligence * Depth estimation * Few shot * Semantic segmentation * Semantics * Super resolution * Transfer learning
