arXiv Machine Learning
FrameScout: Scouting Query-Relevant Frames for Long Video Understanding
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
Hugging Face Trending Papers
Oct 21
Cache Locally, Recompute Selectively for Streaming Video Understanding
Streaming video understanding (SVU) requires video-LLMs to process incoming video continuously while answering user queries with low latency. Recent methods reduce query-time cost by prefilling frames...
arXiv Machine Learning
Oct 22
Cache Locally, Recompute Selectively for Streaming Video Understanding
The paper introduces FrameBlend, a plug‑and‑play technique for streaming video understanding that selectively recomputes key‑value (KV) caches at query time. By re‑prefilling only query‑relevant video tokens, FrameBlend allows shorter attention windows during idle prefill, cutting prefill cost while boosting accuracy. Experiments on three SVU frameworks and five benchmarks show up to 3.9‑point accuracy gains and significant reductions in FLOPs and processing time.
arXiv Computer Vision
Sep 1
RIDGE: Region-Informed Derivative-Guided Evidence Selection for Long Video Understanding
arXiv:2608.29958v1 Announce Type: new Abstract: Long videos contain far more visual content than Large Vision-Language Models (LVLMs) can process under a fixed visual-token budget, making frame selec...
arXiv Computer Vision
Sep 4
CoFiE: Coarse-to-Fine Evidence Selection for Efficient Streaming Video Understanding
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.
arXiv AI
Jul 29
Reasoning with Memory: A Temporal Granularity-Adaptive Framework for Training-Free Long Video Understanding
arXiv:2607. 24794v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) demonstrate superior generalization in fundamental video tasks, restricted context windows limit their long video understanding.
Hugging Face Trending Papers
Sep 16
SVMemAgent: A Streaming Video Memory Agent for Query-Agnostic Online Frame Selection
Most keyframe selection studies focus on offline settings, assuming access to the full video and query in advance. In contrast, real-world streaming scenarios require online frame selection under unkn...