Less is More: Label-Guided Efficient Summarization of Procedural and Instructional Videos

Abstract:Video summarization transforms long videos into concise representations that are easier to document and analyze, especially in high-stakes domains such as surgical training. However, long-form videos often require dense frame processing or supervised training pipelines, which can be computationally expensive and may still miss important procedural content. We present PRISM: Procedural Representation via Integrated Semantic and Multimodal Analysis, a zero-shot and training-free framework for frame-efficient procedural video summarization. PRISM selects fewer than 5% of video frames while retaining over 84% semantic content and improving over baselines by up to 7.5%. Rather than exhaustively processing frames, PRISM uses lightweight visual filtering and dynamically generated procedural labels as semantic anchors to select meaningful frame-label groups. This selective inference design preserves key actions, transitions, and contextual details while reducing the number of visual inputs passed to downstream vision-language captioning stages. We evaluate PRISM on YouCook2 and ActivityNet Captions, with additional studies on keyframe selection benchmarks and surgical video datasets. Across procedural and domain-specific video tasks, PRISM achieves strong semantic alignment and accuracy, suggesting that efficient multimodal video understanding can be achieved by grounding generation in dynamically generated semantic anchors.

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2601.12243 [cs.CV]

(or arXiv:2601.12243v3 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2601.12243 Focus to learn more arXiv-issued DOI via DataCite

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