AI RESEARCH
TRIM: A Self-Supervised Video Summarization Framework Maximizing Temporal Relative Information and Representativeness
arXiv CS.CV
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ArXi:2506.20588v2 Announce Type: replace The increasing ubiquity of video content and the corresponding demand for efficient access to meaningful information have elevated video summarization and video highlights as a vital research area. However, many state-of-the-art methods depend heavily either on supervised annotations or on attention-based models, which are computationally expensive and brittle in the face of distribution shifts that hinder cross-domain applicability across datasets. We