arXiv Computer Vision
KiToke: Distribution-Aware Token Compression for Video Large Language Models
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In video understanding, vision-language models (VLMs) must ingest massive numbers of visual tokens, causing the computational and memory cost of the prefill stage to rise sharply. Such visual sequences are highly redundant along the spatio-temporal dimension, yet a high compression ratio is often accompanied by the loss of critical details.
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Visual Token Coding for Video Multimodal Large Language Models
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Video Codec-Guided Training-Free Token Compression for Video Large Language Models
Video large language models (VLLMs) need long visual token sequences, making token compression essential for efficient inference. This paper introduces a training‑free token compression technique that leverages existing video codec information—such as reference‑frame dependencies, motion vectors, and residuals—to guide low‑cost search priors. By aligning these codec cues with the visual token grid, the method creates compact candidate pools for token protection and localized candidates for temporal token merging, reducing feature comparisons while maintaining performance comparable to or better than the FlashVID baseline across multiple VLLMs and benchmarks.