DTR GAN: dilated temporal relational adversarial network for video summarization
Video summarization targets the challenge of finding the smallest subset of frames, while still conveying the whole story of a given video. Thus it is of great significance for large scale video understanding, allowing efficient processing of the large amount of videos that are uploaded every day. In this paper, we introduce a Dilated Temporal Relational Adversarial Network (DTR GAN) to achieve frame level video summarization. The dilated temporal relational units in the generator aim to exploit multi scale temporal context in order to select key frames. To ensure that the model predicts high quality summaries, we present a discriminator that learns to enhance both the information completeness and compactness via a three player loss. Experiments on the public TVSum dataset demonstrate the effectiveness of the proposed approach.
ACM TUR C