A comprehensive survey of sparse regularization: Fundamental, state-of-the-art methodologies and applications on fault diagnosis
Q Li - Expert Systems with Applications, 2023 - Elsevier
Sparse regularization has been attracting much attention in industrial applications over the
past few decades. By exploiting the latent data structure in low-dimensional subspaces, a …
past few decades. By exploiting the latent data structure in low-dimensional subspaces, a …
3d human mesh estimation from virtual markers
Inspired by the success of volumetric 3D pose estimation, some recent human mesh
estimators propose to estimate 3D skeletons as intermediate representations, from which …
estimators propose to estimate 3D skeletons as intermediate representations, from which …
Tvsum: Summarizing web videos using titles
Video summarization is a challenging problem in part because knowing which part of a
video is important requires prior knowledge about its main topic. We present TVSum, an …
video is important requires prior knowledge about its main topic. We present TVSum, an …
[LIVRE][B] Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep
learning in the field of earth sciences, from four leading voices Deep learning is a …
learning in the field of earth sciences, from four leading voices Deep learning is a …
Sparse modeling for image and vision processing
In recent years, a large amount of multi-disciplinary research has been conducted on sparse
models and their applications. In statistics and machine learning, the sparsity principle is …
models and their applications. In statistics and machine learning, the sparsity principle is …
Perspective on essential information in multivariate curve resolution
We propose to take a new perspective on the construction and interpretation of multivariate
curve resolution (MCR) models for the decomposition of spectral mixture data. We start by …
curve resolution (MCR) models for the decomposition of spectral mixture data. We start by …
A cluster sampling method for image matting via sparse coding
X Feng, X Liang, Z Zhang - Computer Vision–ECCV 2016: 14th European …, 2016 - Springer
In this paper, we present a new image matting algorithm which solves two major problems
encountered by previous sampling-based algorithms. The first is that existing sampling …
encountered by previous sampling-based algorithms. The first is that existing sampling …
Deep metric learning for bioacoustic classification: Overcoming training data scarcity using dynamic triplet loss
Bioacoustic classification often suffers from the lack of labeled data. This hinders the
effective utilization of state-of-the-art deep learning models in bioacoustics. To overcome this …
effective utilization of state-of-the-art deep learning models in bioacoustics. To overcome this …
Multi-level canonical correlation analysis for standard-dose PET image estimation
Positron emission tomography (PET) images are widely used in many clinical applications,
such as tumor detection and brain disorder diagnosis. To obtain PET images of diagnostic …
such as tumor detection and brain disorder diagnosis. To obtain PET images of diagnostic …
DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression
Background Genome-scale models of metabolism and macromolecular expression (ME
models) enable systems-level computation of proteome allocation coupled to metabolic …
models) enable systems-level computation of proteome allocation coupled to metabolic …