Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Background/foreground separation is the first step in video surveillance system to detect
moving objects. Recent research on problem formulations based on decomposition into low …

As a convex relaxation of the rank minimization model, the nuclear norm minimization
(NNM) problem has been attracting significant research interest in recent years. The …

Robust principal component analysis (RPCA) via decomposition into low-rank plus sparse
matrices offers a powerful framework for a large variety of applications such as image …

Low-rank matrix approximation (LRMA)-based methods have made a great success for
grayscale image processing. When handling color images, LRMA either restores each color …

The classification of gigapixel-sized whole slide images (WSIs) with slide-level labels can be
formulated as a multiple-instance-learning (MIL) problem. State-of-the-art models often …

As science and engineering have become increasingly data-driven, the role of optimization
has expanded to touch almost every stage of the data analysis pipeline, from signal and …

Tensor completion recovers missing components of multi-way data. The existing methods
use either the Tucker rank or the CANDECOMP/PARAFAC (CP) rank in low-rank tensor …

How to tackle high dimensionality of data effectively and efficiently is still a challenging issue
in machine learning. Identifying anomalous objects from given data has a broad range of …

Abstract Low Rank Regularization (LRR), in essence, involves introducing a low rank or
approximately low rank assumption to target we aim to learn, which has achieved great …