Summary of Rar: Retrieving and Ranking Augmented Mllms For Visual Recognition, by Ziyu Liu et al.
RAR: Retrieving And Ranking Augmented MLLMs for Visual Recognition
by Ziyu Liu, Zeyi Sun, Yuhang Zang, Wei Li, Pan Zhang, Xiaoyi Dong, Yuanjun Xiong, Dahua Lin, Jiaqi Wang
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper introduces RAR, a method that combines the strengths of CLIP and MLLMs to enhance few-shot/zero-shot recognition abilities for datasets with extensive vocabularies. Specifically, it proposes a multi-modal retriever based on CLIP to create explicit memory for categories beyond the immediate context window. This approach addresses limitations in fine-grained recognition while preserving comprehensive knowledge base. RAR demonstrates significant improvement in performance on various vision-language recognition tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper combines two existing models, CLIP and MLLMs, to make an even better one. It’s like taking two strong friends who are good at different things and working together to be unstoppable! The new model is great at recognizing lots of tiny details in pictures and words, which helps it learn fast and accurately. |
Keywords
* Artificial intelligence * Context window * Few shot * Knowledge base * Multi modal * Zero shot
