Unsupervised Video Summarization via Attention-Driven Adversarial Learning
MMM (1), pp. 492-504, 2020.
This paper presents a new video summarization approach that integrates an attention mechanism to identify the significant parts of the video, and is trained unsupervisingly via generative adversarial learning. Starting from the SUM-GAN model, we first develop an improved version of it (called SUM-GAN-sl) that has a significantly reduced n...More
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Best Paper of MMM, 2020