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Research questionHow can streaming video-language models cut frame-encoding latency while preserving question-relevant evidence?In streaming video understanding, encoding each incoming frame can dominate end-to-end inference time. Pruning visual tokens after encoding cannot recover computation already spent on redundant frames.
AI
Computer Vision
Evaluation & Benchmarks
Image & Video Processing
Inference Optimization
Information Retrieval
Multimodal Models
Latest papersRecent research connected to this question, newest first.CoFiE: Coarse-to-Fine Evidence Selection for Efficient Streaming Video UnderstandingApplies to vision-language models answering user questions from streaming video. The source provides benchmark evidence for separating query-agnostic frame filtering before visual encoding from query-specific evidence selection during language-model prefill, including results on StreamingBench and OvO-Bench.research paper · Sep 3, 2026ShallowStream: Index Shallow then Answer Deep for Streaming Video UnderstandingThe source addresses MLLM-based streaming video understanding with ShallowStream, which builds an always-on index from shallow-layer KV caches and retrieves diverse evidence frames when answering queries. It reports comparable performance to existing streaming methods, along with reductions in per-frame and 10-second end-to-end latency, but the supplied evidence does not specify broader deployment requirements.research paper · Sep 2, 2026
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