Summary of Instance-warp: Saliency Guided Image Warping For Unsupervised Domain Adaptation, by Shen Zheng et al.
Instance-Warp: Saliency Guided Image Warping for Unsupervised Domain Adaptation
by Shen Zheng, Anurag Ghosh, Srinivasa G. Narasimhan
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper tackles the challenge of understanding scenes in adverse conditions like night, rain, or snow by leveraging Unsupervised Domain Adaptation (UDA) techniques. The authors highlight the limitations of existing UDA methods that focus on dominant scene backgrounds, neglecting smaller and often sparse foreground objects. They propose a novel approach to address this issue, aiming to improve the accuracy of scene understanding in such conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Driving can be tough at night, during rain or snowstorms. Right now, there’s a big problem with training computers to recognize scenes in these conditions because we don’t have enough labeled data to work with. One idea is to use large datasets from clear days and adapt them for nighttime or snowy scenes using Unsupervised Domain Adaptation (UDA). However, most UDA methods focus on big background features like roads or sky, which can be very different across domains. As a result, they struggle to recognize smaller objects like people, cars, or signs. |
Keywords
* Artificial intelligence * Domain adaptation * Scene understanding * Unsupervised
