427 citations · 3.3k across the 77 of their papers we have counts for
21 papers · 1 filter
Prompt-In-Prompt Learning for Universal Image Restoration
Zilong Li, Yiming Lei, Chenglong Ma +2
Image restoration, which aims to retrieve and enhance degraded images, is fundamental across a wide range of applications. While conventional deep learning approaches have notably…
Prompted Contextual Transformer for Incomplete-View CT Reconstruction
Chenglong Ma, Zilong Li, Junjun He +3
Incomplete-view computed tomography (CT) can shorten the data acquisition time and allow scanning of large objects, including sparse-view and limited-angle scenarios, each with var…
Energizing Federated Learning via Filter-Aware Attention
Ziyuan Yang, Zerui Shao, Huijie Huangfu +5
Federated learning (FL) is a promising distributed paradigm, eliminating the need for data sharing but facing challenges from data heterogeneity. Personalized parameter generation…
Point, Segment and Count: A Generalized Framework for Object Counting
Zhizhong Huang, Mingliang Dai, Yi Zhang +2
Class-agnostic object counting aims to count all objects in an image with respect to example boxes or class names, \emph{a.k.a} few-shot and zero-shot counting. In this paper, we p…
SIAM: A Simple Alternating Mixer for Video Prediction
Xin Zheng, Ziang Peng, Yuan Cao +2
Video prediction, predicting future frames from the previous ones, has broad applications such as autonomous driving and weather forecasting. Existing state-of-the-art methods typi…
LICO: Explainable Models with Language-Image Consistency
Yiming Lei, Zilong Li, Yangyang Li +2
Interpreting the decisions of deep learning models has been actively studied since the explosion of deep neural networks. One of the most convincing interpretation approaches is sa…
Semantic Latent Decomposition with Normalizing Flows for Face Editing
Binglei Li, Zhizhong Huang, Hongming Shan +1
Navigating in the latent space of StyleGAN has shown effectiveness for face editing. However, the resulting methods usually encounter challenges in complicated navigation due to th…
Learning to Distill Global Representation for Sparse-View CT
Zilong Li, Chenglong Ma, Jie Chen +2
Sparse-view computed tomography (CT) -- using a small number of projections for tomographic reconstruction -- enables much lower radiation dose to patients and accelerated data acq…
Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion Recognition
Jiaxin Ye, Yujie Wei, Xin-Cheng Wen +4
Cross-corpus speech emotion recognition (SER) seeks to generalize the ability of inferring speech emotion from a well-labeled corpus to an unlabeled one, which is a rather challeng…
Online Prototype Learning for Online Continual Learning
Yujie Wei, Jiaxin Ye, Zhizhong Huang +2
Online continual learning (CL) studies the problem of learning continuously from a single-pass data stream while adapting to new data and mitigating catastrophic forgetting. Recent…
ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising
Zhihao Chen, Qi Gao, Yi Zhang +1
While various deep learning methods have been proposed for low-dose computed tomography (CT) denoising, most of them leverage the normal-dose CT images as the ground-truth to super…
Adaptive Nonlinear Latent Transformation for Conditional Face Editing
Zhizhong Huang, Siteng Ma, Junping Zhang +1
Recent works for face editing usually manipulate the latent space of StyleGAN via the linear semantic directions. However, they usually suffer from the entanglement of facial attri…
FreeSeed: Frequency-band-aware and Self-guided Network for Sparse-view CT Reconstruction
Chenglong Ma, Zilong Li, Junping Zhang +2
Sparse-view computed tomography (CT) is a promising solution for expediting the scanning process and mitigating radiation exposure to patients, the reconstructed images, however, c…
FAN-Net: Fourier-Based Adaptive Normalization For Cross-Domain Stroke Lesion Segmentation
Weiyi Yu, Yiming Lei, Hongming Shan
Since stroke is the main cause of various cerebrovascular diseases, deep learning-based stroke lesion segmentation on magnetic resonance (MR) images has attracted considerable atte…
CLIP-Lung: Textual Knowledge-Guided Lung Nodule Malignancy Prediction
Yiming Lei, Zilong Li, Yan Shen +2
Lung nodule malignancy prediction has been enhanced by advanced deep-learning techniques and effective tricks. Nevertheless, current methods are mainly trained with cross-entropy l…
BerDiff: Conditional Bernoulli Diffusion Model for Medical Image Segmentation
Tao Chen, Chenhui Wang, Hongming Shan
Medical image segmentation is a challenging task with inherent ambiguity and high uncertainty, attributed to factors such as unclear tumor boundaries and multiple plausible annotat…
CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization
Qi Gao, Zilong Li, Junping Zhang +2
Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon starvation and electronic noise. Recently, some works have attempted to use diffusion models…
Cross-head Supervision for Crowd Counting with Noisy Annotations
Mingliang Dai, Zhizhong Huang, Jiaqi Gao +2
Noisy annotations such as missing annotations and location shifts often exist in crowd counting datasets due to multi-scale head sizes, high occlusion, etc. These noisy annotations…
Twin Contrastive Learning with Noisy Labels
Zhizhong Huang, Junping Zhang, Hongming Shan
Learning from noisy data is a challenging task that significantly degenerates the model performance. In this paper, we present TCL, a novel twin contrastive learning model to learn…
LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurring
Zhihao Chen, Chuang Niu, Qi Gao +2
This paper studies 3D low-dose computed tomography (CT) imaging. Although various deep learning methods were developed in this context, typically they focus on 2D images and perfor…
CORE: Learning Consistent Ordinal REpresentations for Image Ordinal Estimation
Yiming Lei, Zilong Li, Yangyang Li +2
The goal of image ordinal estimation is to estimate the ordinal label of a given image with a convolutional neural network. Existing methods are mainly based on ordinal regression…