Conference paper Open Access

Unsupervised Video Summarization via Attention-Driven Adversarial Learning

Apostolidis, Evlampios; Adamantidou, Eleni; Metsai, Alexandros; Mezaris, Vasileios; Patras, Ioannis


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    <dct:title>Unsupervised Video Summarization via Attention-Driven Adversarial Learning</dct:title>
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    <dcat:keyword>Video summarization</dcat:keyword>
    <dcat:keyword>Unsupervised learning</dcat:keyword>
    <dcat:keyword>Attention mechanism</dcat:keyword>
    <dcat:keyword>Adversarial learning</dcat:keyword>
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    <dct:description>&lt;p&gt;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 rst develop an improved version of it (called SUM-GAN-sl) that has a significantly reduced number of learned parameters, performs incremental training of the model&amp;#39;s components, and applies a stepwise label-based strategy for updating the adversarial part. Subsequently, we introduce an attention mechanism to SUM-GAN-sl in two ways: i) by integrating an attention layer within the variational auto-encoder (VAE) of the architecture (SUM-GAN-VAAE), and ii) by replacing the VAE with a deterministic attention auto-encoder (SUM-GAN-AAE). Experimental evaluation on two datasets (SumMe and TVSum) documents the contribution of the attention auto-encoder to faster and more stable training of the model, resulting in a signicant performance improvement with respect to the original model and demonstrating the competitiveness of the proposed SUM-GAN-AAE against the state of the art.&amp;nbsp;Software is publicly available at: https://github.com/e-apostolidis/SUM-GAN-AAE&lt;/p&gt;</dct:description>
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