Substantial progress has been made recently on developing provably accurate and efficient
algorithms for low-rank matrix factorization via nonconvex optimization. While conventional …

Conventional neural networks have been demonstrated to be a powerful framework for
background subtraction in video acquired by static cameras. Indeed, the well-known Self …

Computer vision applications based on videos often require the detection of moving objects
in their first step. Background subtraction is then applied in order to separate the background …

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 …

Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …

Robust detection of infrared small and dim targets with highly heterogeneous backgrounds
plays an indispensable role in infrared search and tracking (IRST) system, which is still a …

The goal of continual learning is to find a model that solves multiple learning tasks which are
presented sequentially to the learner. A key challenge in this setting is that the learner may" …

Despite the promising results, tensor robust principal component analysis (TRPCA), which
aims to recover underlying low-rank structure of clean tensor data corrupted with …

Moving Object Segmentation (MOS) is a fundamental task in computer vision. Due to
undesirable variations in the background scene, MOS becomes very challenging for static …

Infrared small target detection (ISTD) is vital for civil and military applications. However,
existing methods often face challenges in coping with complex scenes, discriminating …