Summary of Enhancing Cross-modal Fine-tuning with Gradually Intermediate Modality Generation, by Lincan Cai et al.
Enhancing Cross-Modal Fine-Tuning with Gradually Intermediate Modality Generation
by Lincan Cai, Shuang Li, Wenxuan Ma, Jingxuan Kang, Binhui Xie, Zixun Sun, Chengwei Zhu
First submitted to arxiv on: 13 Jun 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 proposes an end-to-end method called PaRe for enhancing cross-modal fine-tuning of large-scale pretrained models. The method employs a gating mechanism to select key patches from both source and target data, which are then combined using a modality-agnostic Patch Replacement scheme to construct intermediate modalities. This approach allows for gradual bridging of the modality gap, improving stability and transferability, as well as addressing limited data in the target modality. The authors demonstrate superior performance on three challenging benchmarks across more than ten modalities compared to hand-designed, general-purpose, task-specific, and state-of-the-art cross-modal fine-tuning approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn new things by improving how they adapt to different types of data. It’s like teaching a child to recognize new objects by showing them pictures of similar things first. The method, called PaRe, takes advantage of patterns in the data to make it easier for machines to learn from it. This makes it better at recognizing new things and can help with many different applications. |
Keywords
» Artificial intelligence » Fine tuning » Transferability
