Summary of Multi-modal Adapter For Vision-language Models, by Dominykas Seputis et al.
Multi-Modal Adapter for Vision-Language Models
by Dominykas Seputis, Serghei Mihailov, Soham Chatterjee, Zehao Xiao
First submitted to arxiv on: 3 Sep 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 proposes a novel approach called Multi-Modal Adapter for adapting large pre-trained vision-language models like CLIP to specific downstream tasks. By combining visual and textual features using a trainable Multi-Head Attention layer, the method achieves improved generalizability on unseen classes compared to existing adaptation methods. The results are validated through ablations and investigations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This innovative approach can be used to improve the performance of large pre-trained models like CLIP for various image classification tasks without requiring retraining. By adapting both visual and textual representations simultaneously, the Multi-Modal Adapter demonstrates better generalizability than previous methods that adapt individual modalities separately. |
Keywords
» Artificial intelligence » Image classification » Multi head attention » Multi modal
