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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Data or Language Supervision: What Makes CLIP Better than DINO?
%A Liu, Yiming
%A Zhang, Yuhui
%A Ghosh, Dhruba
%A Schmidt, Ludwig
%A Yeung-Levy, Serena
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F liu-etal-2025-data-language
%X 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.
%U https://aclanthology.org/2025.findings-emnlp.98/
%P 1868-1874

Markdown (Informal)

[Data or Language Supervision: What Makes CLIP Better than DINO?](https://aclanthology.org/2025.findings-emnlp.98/) (Liu et al., Findings 2025)

• Data or Language Supervision: What Makes CLIP Better than DINO? (Liu et al., Findings 2025)

ACL

• Yiming Liu, Yuhui Zhang, Dhruba Ghosh, Ludwig Schmidt, and Serena Yeung-Levy. 2025. Data or Language Supervision: What Makes CLIP Better than DINO?. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 1868–1874, Suzhou, China. Association for Computational Linguistics.