OTT-Vid uses optimal transport with non-uniform token mass and locality-aware costs to dynamically allocate compression budgets across video frames, retaining 95.8% VQA and 73.9% VTG performance at 10% token retention.
An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models
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FastOCR dynamically selects a small subset of visual tokens per decoding step using focal-guided pruning and cross-step reuse, retaining 98% accuracy on Qwen2.5-VL while attending to only 5% of tokens and cutting attention latency by 3x.
citing papers explorer
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OTT-Vid: Optimal Transport Temporal Token Compression for Video Large Language Models
OTT-Vid uses optimal transport with non-uniform token mass and locality-aware costs to dynamically allocate compression budgets across video frames, retaining 95.8% VQA and 73.9% VTG performance at 10% token retention.
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FastOCR: Dynamic Visual Fixation via KV Cache Pruning for Efficient Document Parsing
FastOCR dynamically selects a small subset of visual tokens per decoding step using focal-guided pruning and cross-step reuse, retaining 98% accuracy on Qwen2.5-VL while attending to only 5% of tokens and cutting attention latency by 3x.