Masked Autoencoder for Unsupervised Video Summarization
Minho Shim Taeoh Kim Jinhyung Kim Dongyoon Wee
Summarizing a video requires a diverse understanding of the video, ranging
from recognizing scenes to evaluating how much each frame is essential enough
to be selected as a summary. Self-supervised learning (SSL) is acknowledged for
its robustness and flexibility to multiple downstream tasks, but the video SSL
has not shown its value for dense understanding tasks like video summarization.
We claim an unsupervised autoencoder with sufficient self-supervised learning
does not need any extra downstream architecture design or fine-tuning weights
to be utilized as a video summarization model. The proposed method to evaluate
the importance score of each frame takes advantage of the reconstruction score
of the autoencoder's decoder. We evaluate the method in major unsupervised
video summarization benchmarks to show its effectiveness under various
experimental settings.