Recently, increased computational power and data availability, as well as algorithmic
advances, have led machine learning (ML) techniques to impressive results in regression …

Object detection can be regarded as one of the most fundamental and challenging visual
recognition task in computer vision and it has received great attention over the past few …

We present TensoRF, a novel approach to model and reconstruct radiance fields. Unlike
NeRF that purely uses MLPs, we model the radiance field of a scene as a 4D tensor, which …

Object detection is one of the most important and challenging branches of computer vision,
which has been widely applied in people's life, such as monitoring security, autonomous …

In this paper, we consider the Tensor Robust Principal Component Analysis (TRPCA)
problem, which aims to exactly recover the low-rank and sparse components from their sum …

Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …

Existing methods for tensor completion (TC) have limited ability for characterizing low-rank
(LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes …

In this paper we propose a highly efficient and very accurate deep learning method for
estimating the propagation pathloss from a point (transmitter location) to any point on a …

This paper studies the Tensor Robust Principal Component (TRPCA) problem which
extends the known Robust PCA to the tensor case. Our model is based on a new tensor …

The widespread use of multisensor technology and the emergence of big data sets have
highlighted the limitations of standard flat-view matrix models and the necessity to move …