Summary of Toward Modality Gap: Vision Prototype Learning For Weakly-supervised Semantic Segmentation with Clip, by Zhongxing Xu et al.
Toward Modality Gap: Vision Prototype Learning for Weakly-supervised Semantic Segmentation with CLIP
by Zhongxing Xu, Feilong Tang, Zhe Chen, Yingxue Su, Zhiyi Zhao, Ge Zhang, Jionglong Su, Zongyuan Ge
First submitted to arxiv on: 27 Dec 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 A new approach in Weakly Supervised Semantic Segmentation (WSSS) combines Contrastive Language-Image Pre-training (CLIP) with Vision Prototype Learning (VPL) to bridge the modality gap between text and vision spaces. Existing methods focus on optimizing input text prompts, but this doesn’t effectively align text prototypes with pixel-level vision features. The proposed VPL framework introduces more representative vision prototypes and a regional semantic contrast module to capture high-quality localization maps. Experimental results show state-of-the-art performance on two benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary WSSS is an important area of research that tries to match words with images without actually labeling every pixel in the image. The problem is that words are very different from images, so it’s hard to get them to match up well. A new approach called Contrastive Language-Image Pre-training (CLIP) helps a bit, but it still doesn’t work perfectly. The researchers have come up with a new way of doing things, which they call Vision Prototype Learning (VPL). It tries to learn more about what images look like and match them better with words. They also added some extra steps to make the matching even better. When they tested it, it worked really well on two big datasets. |
Keywords
» Artificial intelligence » Semantic segmentation » Supervised
