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VLMs struggle with emotion recognition not because they lack visual acuity, but because pre-training exacerbates dataset biases and sparse temporal sampling misses crucial micro-expressions.
Current text-to-image evaluation metrics miss critical clinical errors, but CSEval uses language models to catch them.
