arXiv Computer Vision September 4, 2026 By Jing Jiang, Yiran Ling, Ruonan Li, Dimitrios Stamoulis, Jie Liu

CoFiE: Coarse-to-Fine Evidence Selection for Efficient Streaming Video Understanding

Read the original on arXiv Computer Vision →

CoFiE introduces a two‑stage evidence selection framework for streaming video understanding, separating a coarse, query‑agnostic filtering of visually distinctive frames from a fine, query‑specific refinement during LLM prefill. By filtering out redundant frames before expensive vision encoding, CoFiE reduces end‑to‑end latency while maintaining high accuracy. The method achieves state‑of‑the‑art performance on benchmarks such as StreamingBench and OvO‑Bench, improving accuracy by up to 3.15% and inference speed by up to 2.54× compared to prior approaches.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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