Summary of Cross-modal Adapter: Parameter-efficient Transfer Learning Approach For Vision-language Models, by Juncheng Yang et al.
Cross-Modal Adapter: Parameter-Efficient Transfer Learning Approach for Vision-Language Models
by Juncheng Yang, Zuchao Li, Shuai Xie, Weiping Zhu, Wei Yu, Shijun Li
First submitted to arxiv on: 19 Apr 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 introduces a novel approach to adapter-based transfer learning for vision-language models. The proposed method, XMAdapter, establishes cache models for both text and image modalities and leverages retrieval through visual-language bimodal information to gather clues for inference. By dynamically adjusting the affinity ratio, XMAdapter achieves cross-modal fusion, decoupling different modal similarities to assess their respective contributions. Additionally, it explores hard samples based on differences in cross-modal affinity and enhances model performance through adaptive adjustment of sample learning intensity. Experimental results on benchmark datasets demonstrate that XMAdapter outperforms previous adapter-based methods significantly regarding accuracy, generalization, and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps improve how computers understand text and images together. It creates a new way to learn from existing knowledge without needing as much training data. The method, called XMAdapter, uses both text and image information to make better predictions. By mixing the two types of information in a smart way, XMAdapter can adapt to new situations more effectively than previous methods. This leads to higher accuracy, better generalization, and faster processing times. |
Keywords
* Artificial intelligence * Generalization * Inference * Transfer learning
