Recently, Self-Supervised Representation Learning (SSRL) has attracted much attention in
the field of computer vision, speech, natural language processing (NLP), and recently, with …

Self-supervised video-based action recognition is a challenging task, which needs to extract
the principal information characterizing the action from content-diversified videos over large …

Recently, self-supervised video representation learning based on Masked Video Modeling
(MVM) has demonstrated promising results for action recognition. However, existing …

While recognition accuracies of video classification models trained on conventional
benchmarks are gradually saturating, recent studies raise alarm about the learned …

In recent years, the machine learning community has devoted an increasing attention to self-
supervised learning. The performance gap between supervised and self-supervised has …

Clustering is a ubiquitous tool in unsupervised learning. Most of the existing self-supervised
representation learning methods typically cluster samples based on visually dominant …

Contrastive representation learning has proven to be an effective self-supervised learning
method for images and videos. Most successful approaches are based on Noise Contrastive …

Audio-visual correlation learning aims to capture and understand natural phenomena
between audio and visual data. The rapid growth of Deep Learning propelled the …

Currently, self-supervised learning has shown effectiveness in solving data labeling issues.
Its success mainly depends on having access to large, high-quality datasets with diverse …

We have witnessed impressive advances in video action understanding. Increased dataset
sizes, variability, and computation availability have enabled leaps in performance and task …