Summary of Understanding Retrieval-augmented Task Adaptation For Vision-language Models, by Yifei Ming et al.
Understanding Retrieval-Augmented Task Adaptation for Vision-Language Models
by Yifei Ming, Yixuan Li
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 investigates how pre-trained contrastive vision-language models adapt to fine-tuned datasets with limited representation during initial training. The authors examine recent approaches using web-scale databases for retrieval-augmented adaptation, which have shown promise in low-data scenarios. To better understand the impact of retrieval on model adaptation, they conduct a systematic study highlighting the roles of key components, including uni-modal and cross-modal retrieval, and the importance of logit ensemble for effective adaptation. Their findings provide new insights and theoretical underpinnings to support empirical observations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how pre-trained models adapt to new data when some categories weren’t well-represented during initial training. Researchers have tried using big databases to help fine-tune the model, but we still don’t fully understand what’s happening. This study tries to figure out which parts of this process are most important and why they work. By looking at different ways of retrieving information from these databases, the authors provide new insights that can help improve our understanding of how models adapt. |
