Rethinking Model Selection in VLM Through the Lens of Gromov-Wasserstein Distance
Vision-Language Models (VLMs) have enhanced traditional LLMs with visual capabilities through the integration of vision encoders. While recent works have explored various combinations of vision encoders and LLMs, there still lacks a principled understanding of what makes a vision encoder suitable for VLM alignment. In this paper, we systematically investigate this question via comprehensive experiments on a curated collection of 19 pre-trained vision encoders from diverse sources. We first demonstrate that common practices, such as choosing encoders with the largest size or highest zero-shot accuracy, consistently fail to identify optimal models. In fact, these metrics show only weak to m...
| Authors | Muyang Li, Yucheng Liu, Jianbo Ma et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 May 2026 |
| Last updated at source | 2 May 2026 |
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