AI RESEARCH
UrbanAlign: Post-hoc Semantic Calibration for VLM-Human Preference Alignment
arXiv CS.CV
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ArXi:2602.19442v3 Announce Type: replace Vision-language models (VLMs) can describe urban scenes in rich detail, yet consistently fail to produce reliable human preference labels in domain-specific tasks such as safety assessment and aesthetic evaluation. The standard fix, fine-tuning or RLHF, requires large-scale annotations and model re