CoCoA-Mix: Confusion-and-Confidence-Aware Mixture Model for Context Optimization
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| Format: | Preprint |
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2025
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| _version_ | 1866908399407464448 |
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| author | Hong, Dasol Lee, Wooju Myung, Hyun |
| author_facet | Hong, Dasol Lee, Wooju Myung, Hyun |
| contents | Prompt tuning, which adapts vision-language models by freezing model parameters and optimizing only the prompt, has proven effective for task-specific adaptations. The core challenge in prompt tuning is improving specialization for a specific task and generalization for unseen domains. However, frozen encoders often produce misaligned features, leading to confusion between classes and limiting specialization. To overcome this issue, we propose a confusion-aware loss (CoA-loss) that improves specialization by refining the decision boundaries between confusing classes. Additionally, we mathematically demonstrate that a mixture model can enhance generalization without compromising specialization. This is achieved using confidence-aware weights (CoA-weights), which adjust the weights of each prediction in the mixture model based on its confidence within the class domains. Extensive experiments show that CoCoA-Mix, a mixture model with CoA-loss and CoA-weights, outperforms state-of-the-art methods by enhancing specialization and generalization. Our code is publicly available at https://github.com/url-kaist/CoCoA-Mix. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07484 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CoCoA-Mix: Confusion-and-Confidence-Aware Mixture Model for Context Optimization Hong, Dasol Lee, Wooju Myung, Hyun Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning I.2.6; I.5.2 Prompt tuning, which adapts vision-language models by freezing model parameters and optimizing only the prompt, has proven effective for task-specific adaptations. The core challenge in prompt tuning is improving specialization for a specific task and generalization for unseen domains. However, frozen encoders often produce misaligned features, leading to confusion between classes and limiting specialization. To overcome this issue, we propose a confusion-aware loss (CoA-loss) that improves specialization by refining the decision boundaries between confusing classes. Additionally, we mathematically demonstrate that a mixture model can enhance generalization without compromising specialization. This is achieved using confidence-aware weights (CoA-weights), which adjust the weights of each prediction in the mixture model based on its confidence within the class domains. Extensive experiments show that CoCoA-Mix, a mixture model with CoA-loss and CoA-weights, outperforms state-of-the-art methods by enhancing specialization and generalization. Our code is publicly available at https://github.com/url-kaist/CoCoA-Mix. |
| title | CoCoA-Mix: Confusion-and-Confidence-Aware Mixture Model for Context Optimization |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning I.2.6; I.5.2 |
| url | https://arxiv.org/abs/2506.07484 |