Combining Adversarial and Reinforcement Learning for Video Thumbnail Selection

ABSTRACTThis paper presents a new method for unsupervised video thumbnail selection. The developed network architecture selects video thumbnails based on two criteria: the representativeness and the aesthetic quality of their visual content. Training relies on a combination of adversarial and reinforcement learning. The former is used to train a discriminator, whose goal is to distinguish the original from a reconstructed version of the video based on a small set of candidate thumbnails. The discriminator's feedback is a measure of the representativeness of the selected thumbnails. This measure is combined with estimates about the aesthetic quality of the thumbnails (made using a SoA Fully Convolutional Network) to form a reward and train the thumbnail selector via reinforcement learning. Experiments on two datasets (OVP and Youtube) show the competitiveness of the proposed method against other SoA approaches. An ablation study with respect to the adopted thumbnail selection criteria documents the importance of considering the aesthetics, and the contribution of this information when used in combination with measures about the representativeness of the visual content.