AVLLMs encode audio semantics in middle layers but suppress them in final text outputs when audio conflicts with vision, due to training that largely inherits from vision-language base models.
Clair: Evaluating image captions with large language models
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ClaimDiff-RL introduces reference-conditioned atomic claim differences verified by a multimodal judge as the reward signal for fine-grained RL in long-form image captioning.
VC-Inspector introduces a lightweight open-source LMM and a controllable factual-error generation framework that achieves state-of-the-art correlation with human judgments on reference-free video caption evaluation.
citing papers explorer
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Do Audio-Visual Large Language Models Really See and Hear?
AVLLMs encode audio semantics in middle layers but suppress them in final text outputs when audio conflicts with vision, due to training that largely inherits from vision-language base models.
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ClaimDiff-RL: Fine-Grained Caption Reinforcement Learning through Visual Claim Comparison
ClaimDiff-RL introduces reference-conditioned atomic claim differences verified by a multimodal judge as the reward signal for fine-grained RL in long-form image captioning.
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VC-Inspector: Advancing Reference-free Evaluation of Video Captions with Factual Analysis
VC-Inspector introduces a lightweight open-source LMM and a controllable factual-error generation framework that achieves state-of-the-art correlation with human judgments on reference-free video caption evaluation.