Summarizing Videos using Concentrated Attention and Considering the Uniqueness and Diversity of the Video Frames

In this work, we describe a new method for unsupervised video summarization.

Method

To overcome limitations of existing unsupervised video summarization approaches, that relate to the unstable training of Generator-Discriminator architectures, the use of RNNs for modeling long-range frames’ dependencies and the ability to parallelize the training process of RNN-based network architectures, the developed method relies solely on the use of a self-attention mechanism to estimate the importance of video frames. Instead of simply modeling the frames’ dependencies based on global attention, our method integrates a concentrated attention mechanism that is able to focus on non-overlapping blocks in the main diagonal of the attention matrix, and to enrich the existing information by extracting and exploiting knowledge about the uniqueness and diversity of the associated frames of the video.

Results

In this way, our method makes better estimates about the significance of different parts of the video, and drastically reduces the number of learnable parameters. Experimental evaluations using two benchmarking datasets (SumMe and TVSum) show the competitiveness of the proposed method against other state-of-the-art unsupervised summarization approaches, and demonstrate its ability to produce video summaries that are very close to the human preferences.