In recent years, great advances in pre-trained language models (PLMs) ha...
This paper provides a novel framework for single-domain generalized obje...
Large Language Models (LLMs) such as ChatGPT, have gained significant
at...
Recently, many forms of audio industrial applications, such as sound
mon...
Synthetic aperture radar tomographic imaging reconstructs the
three-dime...
Knowledge Distillation (KD) transfers the knowledge from a high-capacity...
Domain generalization (DG) enables generalizing a learning machine from
...
A main challenge faced in the deep learning-based Underwater Image
Enhan...
Knowledge Distillation (KD) transfers the knowledge from a high-capacity...
Domain Adaptive Object Detection (DAOD) focuses on improving the
general...
Federated Learning (FL) is developed to learn a single global model acro...
Anomaly Detection (AD) on medical images enables a model to recognize an...
Acoustic source localization has been applied in different fields, such ...
Synthesizing a subject-specific pathology-free image from a pathological...
We devise a new regularization, called self-verification, for image
deno...
Medical imaging datasets usually exhibit domain shift due to the variati...
Deep Q Network (DQN) firstly kicked the door of deep reinforcement learn...
The analysis of organ vessels is essential for computer-aided diagnosis ...
Hyperspectral image fusion (HIF) is critical to a wide range of applicat...
Recent works on two-stage cross-domain detection have widely explored th...
Retinal artery/vein (A/V) classification is a critical technique for
dia...
The goal of unsupervised anomaly segmentation (UAS) is to detect the
pix...
SAR (Synthetic Aperture Radar) tomography reconstructs 3-D volumes from
...
For underwater applications, the effects of light absorption and scatter...
To improve the quality of underwater images, various kinds of underwater...
Despite deep convolutional neural networks achieved impressive progress ...
The effectiveness of existing denoising algorithms typically relies on
a...
Standard segmentation of medical images based on full-supervised
convolu...
Recent advances in adaptive object detection have achieved compelling re...
We propose a new framework called Noise2Blur (N2B) for training robust i...
The performance of a deep neural network is highly dependent on its trai...
Automatic and accurate segmentation of the ventricles and myocardium fro...
Graph alignment, also known as network alignment, is a fundamental task ...
We present a supervised technique for learning to remove rain from image...
Existing methods for single images raindrop removal either have poor
rob...
We propose a simple yet effective deep tree-structured fusion model base...
Unsupervised domain adaptation (UDA) transfers knowledge from a label-ri...
The analysis of lesion within medical image data is desirable for effici...
Existing deep convolutional neural networks have found major success in ...
The estimation of crowd count in images has a wide range of applications...
The need for fast acquisition and automatic analysis of MRI data is grow...
In multi-contrast magnetic resonance imaging (MRI), compressed sensing t...
Compressed sensing MRI is a classic inverse problem in the field of
comp...
Compressed sensing (CS) theory assures us that we can accurately reconst...
Compressed sensing for magnetic resonance imaging (CS-MRI) exploits imag...
In this letter, an effective image saliency detection method is proposed...