Imperfect Vision Encoders: Efficient and Robust Tuning for Vision-Language Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929432847974400 |
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| author | Panos, Aristeidis Aljundi, Rahaf Reino, Daniel Olmeda Turner, Richard E |
| author_facet | Panos, Aristeidis Aljundi, Rahaf Reino, Daniel Olmeda Turner, Richard E |
| contents | Vision language models (VLMs) demonstrate impressive capabilities in visual question answering and image captioning, acting as a crucial link between visual and language models. However, existing open-source VLMs heavily rely on pretrained and frozen vision encoders (such as CLIP). Despite CLIP's robustness across diverse domains, it still exhibits non-negligible image understanding errors. These errors propagate to the VLM responses, resulting in sub-optimal performance. In our work, we propose an efficient and robust method for updating vision encoders within VLMs. Our approach selectively and locally updates encoders, leading to substantial performance improvements on data where previous mistakes occurred, while maintaining overall robustness. Furthermore, we demonstrate the effectiveness of our method during continual few-shot updates. Theoretical grounding, generality, and computational efficiency characterize our approach. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_16526 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Imperfect Vision Encoders: Efficient and Robust Tuning for Vision-Language Models Panos, Aristeidis Aljundi, Rahaf Reino, Daniel Olmeda Turner, Richard E Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Machine Learning Vision language models (VLMs) demonstrate impressive capabilities in visual question answering and image captioning, acting as a crucial link between visual and language models. However, existing open-source VLMs heavily rely on pretrained and frozen vision encoders (such as CLIP). Despite CLIP's robustness across diverse domains, it still exhibits non-negligible image understanding errors. These errors propagate to the VLM responses, resulting in sub-optimal performance. In our work, we propose an efficient and robust method for updating vision encoders within VLMs. Our approach selectively and locally updates encoders, leading to substantial performance improvements on data where previous mistakes occurred, while maintaining overall robustness. Furthermore, we demonstrate the effectiveness of our method during continual few-shot updates. Theoretical grounding, generality, and computational efficiency characterize our approach. |
| title | Imperfect Vision Encoders: Efficient and Robust Tuning for Vision-Language Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2407.16526 |