Current VLMs score well on Chinese-art recognition QA but collapse on style-to-period inference, expert-style long-form appreciation, and authenticity discrimination under visual confounds.
In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), pages 11569–11579
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CArtBench: Evaluating Vision-Language Models on Chinese Art Understanding, Interpretation, and Authenticity
Current VLMs score well on Chinese-art recognition QA but collapse on style-to-period inference, expert-style long-form appreciation, and authenticity discrimination under visual confounds.