TRIMMER proposes a self-supervised RL method for video summarization that uses entropy-based rewards to capture temporal dynamics and semantic diversity, claiming SOTA results among unsupervised approaches.
Making a long story short: A multi-importance fast-forwarding egocentric videos with the emphasis on relevant objects,
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TRIMMER: A New Paradigm for Video Summarization through Self-Supervised Reinforcement Learning
TRIMMER proposes a self-supervised RL method for video summarization that uses entropy-based rewards to capture temporal dynamics and semantic diversity, claiming SOTA results among unsupervised approaches.