Coverage-Driven Adaptive Keyframe Selection for Video Understanding
AI Summary
The paper introduces CSES, a training-free semantic keyframe selector for long-video understanding. It estimates the prominence of the frame-query relevance profile to adaptively decide how many frames to score, then treats selection as a coverage problem balancing semantic relevance, temporal redundancy, and visual redundancy. Acquisition and selection stop when coverage saturates. Across four LVLMs and two benchmarks, the authors report comparable accuracy while scoring 4–13x fewer frames, selecting 18.4%–20.5% fewer keyframes than baselines, and achieving a 3.1–5.4x speedup in frame selection.
Why it's worth reading
As long-video systems increasingly bottleneck on frame processing before LVLM inference, CSES offers a concrete way to reduce both frame scoring and selected-input costs without additional training.
Deep Read
1. What happened
Original facts: The paper proposes CSES, a training-free method for semantic keyframe selection in long-video understanding. It adaptively determines both how many frames to score and how many keyframes to pass to the LVLM.
2. Core technology
Original facts: CSES estimates the prominence of the frame-query relevance profile to guide active acquisition and adjust temporal coverage. It then formulates selection as a coverage objective combining semantic relevance, temporal redundancy, and visual redundancy. Both processes terminate when coverage saturates. Analysis: The approach turns frame inspection into an information-gain process, reducing the need to densely score every candidate frame.
3. Key evidence and numbers
Original facts: The abstract reports experiments with four LVLMs on two benchmarks. CSES preserves accuracy while scoring 4–13x fewer frames than existing baselines, selecting 18.4%–20.5% fewer input keyframes, and accelerating frame selection by 3.1–5.4x. The objective is monotone and submodular, allowing greedy optimization with a standard approximation guarantee. Unverified inference: The abstract does not clarify whether “preserves accuracy” means exact equality, statistical equivalence, or near-parity across tasks.
4. Why it matters
Analysis: Long-video cost comes from both LVLM inference on selected frames and the upstream scoring and preprocessing of candidate frames. If the gains generalize across models, query types, and video lengths, CSES could improve the cost-latency tradeoff of video QA, retrieval, and video agents.
5. Practical impact
Analysis: In a production pipeline, CSES could serve as a query-conditioned frame-budget controller before LVLM calls. It should be particularly useful when relevant content is sparse or video duration varies substantially. Evaluation should separately measure decoding, feature extraction, relevance scoring, data transfer, and LVLM inference rather than treating selector speedup as total-system speedup.
6. Limitations and uncertainty
Original facts: The supplied information does not name the four LVLMs or two benchmarks, nor does it provide dataset sizes, accuracy deltas, scoring-model details, or hardware settings. Analysis: Training-free does not mean cost-free; active acquisition may still require visual features or relevance computation. Performance may depend on query difficulty, shot-change density, and the temporal distribution of relevant content. The greedy guarantee applies to the formal coverage objective, not directly to downstream LVLM accuracy.
7. Original sources
- arXiv abstract and paper page
- arXiv ID:
2608.00714 - Publication date: 2026-08-01