Data or Language Supervision: What Makes CLIP Better than DINO?
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911208548859904 |
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| author | Liu, Yiming Zhang, Yuhui Ghosh, Dhruba Schmidt, Ludwig Yeung-Levy, Serena |
| author_facet | Liu, Yiming Zhang, Yuhui Ghosh, Dhruba Schmidt, Ludwig Yeung-Levy, Serena |
| contents | CLIP outperforms self-supervised models like DINO as vision encoders for vision-language models (VLMs), but it remains unclear whether this advantage stems from CLIP's language supervision or its much larger training data. To disentangle these factors, we pre-train CLIP and DINO under controlled settings -- using the same architecture, dataset, and training configuration -- achieving similar ImageNet accuracy. Embedding analysis shows that CLIP captures high-level semantics (e.g., object categories, text), while DINO is more responsive to low-level features like colors and styles. When integrated into VLMs and evaluated on 20 VQA benchmarks, CLIP excels at text-intensive tasks, while DINO slightly outperforms on vision-centric ones. Variants of language supervision (e.g., sigmoid loss, pre-trained language encoders) yield limited gains. Our findings provide scientific insights into vision encoder design and its impact on VLM performance. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_11835 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data or Language Supervision: What Makes CLIP Better than DINO? Liu, Yiming Zhang, Yuhui Ghosh, Dhruba Schmidt, Ludwig Yeung-Levy, Serena Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Machine Learning Multimedia CLIP outperforms self-supervised models like DINO as vision encoders for vision-language models (VLMs), but it remains unclear whether this advantage stems from CLIP's language supervision or its much larger training data. To disentangle these factors, we pre-train CLIP and DINO under controlled settings -- using the same architecture, dataset, and training configuration -- achieving similar ImageNet accuracy. Embedding analysis shows that CLIP captures high-level semantics (e.g., object categories, text), while DINO is more responsive to low-level features like colors and styles. When integrated into VLMs and evaluated on 20 VQA benchmarks, CLIP excels at text-intensive tasks, while DINO slightly outperforms on vision-centric ones. Variants of language supervision (e.g., sigmoid loss, pre-trained language encoders) yield limited gains. Our findings provide scientific insights into vision encoder design and its impact on VLM performance. |
| title | Data or Language Supervision: What Makes CLIP Better than DINO? |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Machine Learning Multimedia |
| url | https://arxiv.org/abs/2510.11835 |