AI RESEARCH
TRIMMER: A New Paradigm for Video Summarization through Self-Supervised Reinforcement Learning
arXiv CS.CV
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ArXi:2605.01659v1 Announce Type: new The rapid growth of video content across domains such as surveillance, education, and social media has made efficient content understanding increasingly critical. Video summarization addresses this challenge by generating concise yet semantically meaningful representations, but existing approaches often rely on expensive manual annotations, struggle to generalize across domains, and incur significant computational costs due to complex architectures.