Combining Global and Local Attention with Positional Encoding for Video Summarization

This paper presents a new method for supervised video summarization. To overcome drawbacks of existing RNN-based summarization architectures, that relate to the modeling of long-range frames’ dependencies and the ability to parallelize the training process, the developed model re-lies on the use of self-attention mechanisms to estimate the importance of video frames. Contrary to previous attention...