AI RESEARCH
BiCLIP: Domain Canonicalization via Structured Geometric Transformation
arXiv CS.AI
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ArXi:2603.08942v1 Announce Type: cross Recent advances in vision-language models (VLMs) have nstrated remarkable zero-shot capabilities, yet adapting these models to specialized domains remains a significant challenge. Building on recent theoretical insights suggesting that independently trained VLMs are related by a canonical transformation, we extend this understanding to the concept of domains. We hypothesize that image features across disparate domains are related by a canonicalized geometric transformation that can be recovered using a small set of anchors.