Self-supervised representation learning (SSRL) methods aim to provide powerful, deep
feature learning without the requirement of large annotated data sets, thus alleviating the …

Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance
systems, enabling the temporal or spatial identification of anomalous events within videos …

To date, most existing self-supervised learning methods are designed and optimized for
image classification. These pre-trained models can be sub-optimal for dense prediction …

Contrastive learning has become a key component of self-supervised learning approaches
for computer vision. By learning to embed two augmented versions of the same image close …

This paper presents Prototypical Contrastive Learning (PCL), an unsupervised
representation learning method that addresses the fundamental limitations of instance-wise …

MoCo is effective for unsupervised image representation learning. In this paper, we propose
VideoMoCo for unsupervised video representation learning. Given a video sequence as an …

Self-supervised visual representation learning has seen huge progress recently, but no
large scale evaluation has compared the many models now available. We evaluate the …

We devise deep nearest centroids (DNC), a conceptually elegant yet surprisingly effective
network for large-scale visual recognition, by revisiting Nearest Centroids, one of the most …

This paper investigates two techniques for developing efficient self-supervised vision
transformers (EsViT) for visual representation learning. First, we show through a …

Recently deep learning methods have shown significant progress in data clustering tasks.
Deep clustering methods (including distance-based methods and subspace-based …