Tensor cascaded-rank minimization in subspace: A unified regime for hyperspectral image low-level vision
Low-rank tensor representation philosophy has enjoyed a reputation in many hyperspectral
image (HSI) low-level vision applications, but previous studies often failed to …
image (HSI) low-level vision applications, but previous studies often failed to …
Robust tensor completion via capped Frobenius norm
Tensor completion (TC) refers to restoring the missing entries in a given tensor by making
use of the low-rank structure. Most existing algorithms have excellent performance in …
use of the low-rank structure. Most existing algorithms have excellent performance in …
Effective tensor completion via element-wise weighted low-rank tensor train with overlapping ket augmentation
Tensor completion methods based on the tensor train (TT) have the issues of inaccurate
weight assignment and ineffective tensor augmentation pre-processing. In this work, we …
weight assignment and ineffective tensor augmentation pre-processing. In this work, we …
-Norm Minimization-Based Robust Matrix Completion Approach for MIMO Radar Target Localization
In this article, we propose a robust matrix completion approach based on-norm minimization
for target localization in sub-Nyquist sampled multiple-input–multiple-output (MIMO) radar …
for target localization in sub-Nyquist sampled multiple-input–multiple-output (MIMO) radar …
Robust matrix completion based on factorization and truncated-quadratic loss function
Robust matrix completion refers to recovering a low-rank matrix given a subset of the entries
corrupted by gross errors, and has various applications since many real-world signals can …
corrupted by gross errors, and has various applications since many real-world signals can …
Ted-net: Dispersal attention for perceiving interaction region in indirectly-contact hoi detection
Y Wang, Q Liu, Y Lei - … on Circuits and Systems for Video …, 2024 - ieeexplore.ieee.org
Human-Object Interaction (HOI) detection is a fertile research ground that merits further
investigation in computer vision, and plays an important role in image high-level semantic …
investigation in computer vision, and plays an important role in image high-level semantic …
Toward compact transformers for end-to-end object detection with decomposed chain tensor structure
P Zhen, X Yan, W Wang, T Hou, H Wei… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
DEtection TRansformer (DETR) is a recently proposed method that streamlines the detection
pipeline and achieves competitive results against two-stage detectors such as Faster …
pipeline and achieves competitive results against two-stage detectors such as Faster …
3d radio imaging under low-rank constraint
Radio frequency (RF) imaging has been of interest for years due to its ability to create
images of targets without being affected by weather and light conditions. The existing …
images of targets without being affected by weather and light conditions. The existing …
Low-rank tensor completion via novel sparsity-inducing regularizers
To alleviate the bias generated by the-norm in the low-rank tensor completion problem,
nonconvex surrogates/regularizers have been suggested to replace the tensor nuclear …
nonconvex surrogates/regularizers have been suggested to replace the tensor nuclear …
Patch tracking-based streaming tensor ring completion for visual data recovery
Tensor completion aims to recover the missing entries of a partially observed tensor by
exploiting its low-rank structure, and has been applied to visual data recovery. In …
exploiting its low-rank structure, and has been applied to visual data recovery. In …