Unsupervised Video Summarization
Hanqing Li Diego Klabjan Jean Utke
This paper introduces a new, unsupervised method for automatic video
summarization using ideas from generative adversarial networks but eliminating
the discriminator, having a simple loss function, and separating training of
different parts of the model. An iterative training strategy is also applied by
alternately training the reconstructor and the frame selector for multiple
iterations. Furthermore, a trainable mask vector is added to the model in
summary generation during training and evaluation. The method also includes an
unsupervised model selection algorithm. Results from experiments on two public
datasets (SumMe and TVSum) and four datasets we created (Soccer, LoL, MLB, and
ShortMLB) demonstrate the effectiveness of each component on the model
performance, particularly the iterative training strategy. Evaluations and
comparisons with the state-of-the-art methods highlight the advantages of the
proposed method in performance, stability, and training efficiency.