Self-Attention Based Generative Adversarial Networks For Unsupervised
  Video Summarization
Maria Nektaria Minaidi Charilaos Papaioannou Alexandros Potamianos
In this paper, we study the problem of producing a comprehensive video
summary following an unsupervised approach that relies on adversarial learning.
We build on a popular method where a Generative Adversarial Network (GAN) is
trained to create representative summaries, indistinguishable from the
originals. The introduction of the attention mechanism into the architecture
for the selection, encoding and decoding of video frames, shows the efficacy of
self-attention and transformer in modeling temporal relationships for video
summarization. We propose the SUM-GAN-AED model that uses a self-attention
mechanism for frame selection, combined with LSTMs for encoding and decoding.
We evaluate the performance of the SUM-GAN-AED model on the SumMe, TVSum and
COGNIMUSE datasets. Experimental results indicate that using a self-attention
mechanism as the frame selection mechanism outperforms the state-of-the-art on
SumMe and leads to comparable to state-of-the-art performance on TVSum and
COGNIMUSE.