SpeGCL: Self-Supervised Graph Spectrum Contrastive Learning Without Positive Samples.

Graph contrastive learning (GCL) has emerged as a powerful method for dealing with noise and fluctuations in graph-structured data, and can be applied to social networks and knowledge graphs. Although various graph augmentation strategies have emerged in the field of GCL, traditional graph convolutional network (GCN) mainly tends to preserve smooth features and has difficulty capturing fine-grained changes between different views. To address the above issue, we first construct Fourier graph neural network (FourierGNN) from the perspective of graph spectrum learning, which captures different frequency components by stacking multiple Fourier graph operations (FGO) layers in Fourier space.

Contrastive Graph Representation Learning with Adversarial Cross-View Reconstruction and Information Bottleneck.

Graph Neural Networks (GNNs) have received extensive research attention due to their powerful information aggregation capabilities. Despite the success of GNNs, most of them suffer from the popularity bias issue in a graph caused by a small number of popular categories. Additionally, real graph datasets always contain incorrect node labels, which hinders GNNs from learning effective node representations.

A Model-Driven Deep Unfolding Method for JPEG Artifacts Removal.

Deep learning-based methods have achieved notable progress in removing blocking artifacts caused by lossy JPEG compression on images. However, most deep learning-based methods handle this task by designing black-box network architectures to directly learn the relationships between the compressed images and their clean versions. These network architectures are always lack of sufficient interpretability, which limits their further improvements in deblocking performance.

Online Rain/Snow Removal From Surveillance Videos.

Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of many surveillance system. Various methods have been investigated extensively, but most only consider consistent rain/snow under stable background scenes. Rain/snow captured from practical surveillance camera, however, is always highly dynamic in time, and those videos also include occasionally transformed background scenes and background motions caused by waving leaves or water surfaces.

Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network.

This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information.

Denoising Hyperspectral Image With Non-i.i.d. Noise Structure.

Hyperspectral image (HSI) denoising has been attracting much research attention in remote sensing area due to its importance in improving the HSI qualities. The existing HSI denoising methods mainly focus on specific spectral and spatial prior knowledge in HSIs, and share a common underlying assumption that the embedded noise in HSI is independent and identically distributed (i.i.

Robust Low-Rank Matrix Factorization Under General Mixture Noise Distributions.

Many computer vision problems can be posed as learning a low-dimensional subspace from high-dimensional data. The low rank matrix factorization (LRMF) represents a commonly utilized subspace learning strategy. Most of the current LRMF techniques are constructed on the optimization problems using L-norm and L-norm losses, which mainly deal with the Laplace and Gaussian noises, respectively.