Summary of Language-driven Visual Consensus For Zero-shot Semantic Segmentation, by Zicheng Zhang et al.
Language-Driven Visual Consensus for Zero-Shot Semantic Segmentation
by Zicheng Zhang, Tong Zhang, Yi Zhu, Jianzhuang Liu, Xiaodan Liang, QiXiang Ye, Wei Ke
First submitted to arxiv on: 13 Mar 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 A novel approach to zero-shot semantic segmentation is proposed, leveraging pre-trained vision-language models like CLIP to align visual features with class embeddings through a transformer decoder. The LDVC method addresses challenges like overfitting on seen classes and small fragmentation in masks by introducing route attention into self-attention to enhance semantic consistency. A vision-language prompting strategy further boosts the generalization capacity of segmentation models for unseen classes, achieving mIoU gains of 4.5 on PASCAL VOC 2012 and 3.6 on COCO-Stuff 164k compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to help computers understand what’s in pictures without being trained specifically for that task. They used a special kind of model called CLIP, which can look at both words and images. The goal was to improve how well this type of model does when it’s shown new things it hasn’t seen before. To do this, they came up with a new approach that helps the model focus on the right parts of the picture and ignore noisy or confusing information. This resulted in significant improvements, making the computer better at understanding what’s in pictures without needing to be trained for every specific thing it might see. |
Keywords
» Artificial intelligence » Attention » Decoder » Generalization » Overfitting » Prompting » Self attention » Semantic segmentation » Transformer » Zero shot
