Summary of Vllavo: Mitigating Visual Gap Through Llms, by Shuhao Chen et al.
VLLaVO: Mitigating Visual Gap through LLMs
by Shuhao Chen, Yulong Zhang, Weisen Jiang, Jiangang Lu, Yu Zhang
First submitted to arxiv on: 6 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 proposed VLLaVO method combines vision-language models with large language models to learn cross-domain invariant knowledge, bridging the gap between training and testing data in visual cross-domain learning. By converting images into detailed textual descriptions using vision-language models, a large language model is then finetuned on source/target domain text generated from an instruction template. The results show that VLLaVO outperforms traditional methods under domain generalization and unsupervised domain adaptation settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to teach a machine to recognize objects in pictures taken with different cameras or lighting conditions. It’s like trying to learn a new language when the words and grammar keep changing! To solve this problem, researchers developed a way to use both images and text together to help machines understand what they’re looking at. The new method, called VLLaVO, uses special computer programs that can talk about pictures in detail. It then uses these descriptions to train another program to recognize objects in different pictures. The results show that this method is really good at recognizing objects even when the pictures look very different. |
Keywords
* Artificial intelligence * Domain adaptation * Domain generalization * Large language model * Unsupervised
