AI RESEARCH
Rethinking Model Selection in VLM Through the Lens of Gromov-Wasserstein Distance
arXiv CS.LG
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ArXi:2605.01325v1 Announce Type: cross 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.