Summary of Lobg:less Overfitting For Better Generalization in Vision-language Model, by Chenhao Ding et al.
LOBG:Less Overfitting for Better Generalization in Vision-Language Model
by Chenhao Ding, Xinyuan Gao, Songlin Dong, Yuhang He, Qiang Wang, Alex Kot, Yihong Gong
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A proposed framework for Vision-Language Models (VLMs), named LOBG, enhances transfer capabilities while addressing significant decline in generalization due to overfitting. The approach utilizes CLIP to filter out fine-grained foreground information and guide prompts with basic visual concepts. Additionally, a structural topology preservation loss at the feature level and hierarchical logit distillation at the output level are employed to mitigate overfitting. Experimental results show improved generalization capabilities and reduced overfitting compared to state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of training Vision-Language Models (VLMs) helps them learn better by reducing mistakes caused by focusing on small details too much. The method, called LOBG, uses a special tool called CLIP to help VLMs focus on the big picture and not get stuck in tiny details. This makes it easier for VLMs to apply what they’ve learned to other tasks. By doing this, LOBG helps VLMs make fewer mistakes when trying new things. |
Keywords
» Artificial intelligence » Distillation » Generalization » Overfitting
