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The AP-LCA approach introduces a novel local cross-modal alignment strategy that utilizes image cropping and similarity-based semantic contribution assessment to precisely map fine-grained descriptions to relevant local image regions.
It is demonstrated that resolution differences can serve as a simple and scalable source of privileged information, providing an effective and efficient approach to on-policy self-distillation for multimodal large language models.
It is observed that attention scores from both vision and text tokens peak at modality separator tokens, suggesting that these separators bridge the two modalities and proposes SepPrune, an efficient, training-free, plug-and-play pruning method that uses the separator token as a unified query to rank and select informative vision tokens.
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