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Xidian University
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Video LLMs can get a free performance boost by using ST-GridPool, a novel technique that enhances visual token representations without any additional training.
Instead of just pruning redundant tokens, ST-SimDiff dramatically cuts MLLM video processing costs by intelligently preserving tokens representing *changes* in the video.
Standard multimodal LLMs can perform surprisingly well on dense prediction tasks like segmentation and depth estimation, without needing any task-specific decoder modules.