Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

Point cloud data have been widely explored due to its superior accuracy and robustness
under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved …

Scale is the primary factor for building a powerful foundation model that could well
generalize to a variety of downstream tasks. However, it is still challenging to train video …

Pre-training video transformers on extra large-scale datasets is generally required to
achieve premier performance on relatively small datasets. In this paper, we show that video …

Benefiting from masked visual modeling, self-supervised video representation learning has
achieved remarkable progress. However, existing methods focus on learning …

Video self-supervised learning is a challenging task, which requires significant expressive
power from the model to leverage rich spatial-temporal knowledge and generate effective …

In this paper, we propose self-supervised training for video transformers using unlabeled
video data. From a given video, we create local and global spatiotemporal views with …

There has been significant progress in Masked Image Modeling (MIM). Existing MIM
methods can be broadly categorized into two groups based on the reconstruction target …

Human action recognition has recently become one ofthe popular research topics in the
computer vision community. Various 3D-CNN based methods have been presented to tackle …

Abstract This paper presents Probabilistic Video Contrastive Learning, a self-supervised
representation learning method that bridges contrastive learning with probabilistic …