Hyperspectral images (HSIs) provide detailed spectral information through hundreds of
(narrow) spectral channels (also known as dimensionality or bands), which can be used to …

A graph is one of important mathematical tools to describe ubiquitous relations. In the
classical graph theory and some applications, graphs are generally provided in advance, or …

Recent studies show that Vision Transformers (ViTs) exhibit strong robustness against
various corruptions. Although this property is partly attributed to the self-attention …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

In this paper, we propose an online clustering method called Contrastive Clustering (CC)
which explicitly performs the instance-and cluster-level contrastive learning. To be specific …

An integral part of video analysis and surveillance is temporal activity detection, which
means to simultaneously recognize and localize activities in long untrimmed videos …

Can we automatically group images into semantically meaningful clusters when ground-
truth annotations are absent? The task of unsupervised image classification remains an …

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

Multiview clustering partitions data into different groups according to their heterogeneous
features. Most existing methods degenerate the applicability of models due to their …

We present a novel clustering objective that learns a neural network classifier from scratch,
given only unlabelled data samples. The model discovers clusters that accurately match …