187 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Fudan UniversityCN59 papers
- Frontiers Center for Brain Science of the Ministry of EducationCN8 papers
- Rensselaer Polytechnic InstituteUS7 papers
- Shanghai Jiao Tong UniversityCN7 papers
- Chinese Academy of SciencesCN6 papers
- Sichuan UniversityCN6 papers
- Tsinghua UniversityCN5 papers
- Chongqing Normal UniversityCN4 papers
- East China Normal UniversityCN4 papers
- Huashan HospitalCN4 papers
- Shanxi UniversityCN4 papers
- Humboldt-Universität zu BerlinDE3 papers
31 papers · 1 filter
IR-Flow: Bridging Discriminative and Generative Image Restoration via Rectified Flow
Zihao Fan, Xin Lu, Jie Xiao +3
In image restoration, single-step discriminative mappings often lack fine details via expectation learning, whereas generative paradigms suffer from inefficient multi-step sampling…
Uncertainty-Supervised Interpretable and Robust Evidential Segmentation
Yuzhu Li, An Sui, Fuping Wu +1
Uncertainty estimation has been widely studied in medical image segmentation as a tool to provide reliability, particularly in deep learning approaches. However, previous methods g…
HiDiff: Hybrid Diffusion Framework for Medical Image Segmentation
Tao Chen, Chenhui Wang, Zhihao Chen +2
Medical image segmentation has been significantly advanced with the rapid development of deep learning (DL) techniques. Existing DL-based segmentation models are typically discrimi…
Image-Text-Image Knowledge Transfer for Lifelong Person Re-Identification with Hybrid Clothing States
Qizao Wang, Xuelin Qian, Bin Li +2
With the continuous expansion of intelligent surveillance networks, lifelong person re-identification (LReID) has received widespread attention, pursuing the need of self-evolution…
Bayesian Unsupervised Disentanglement of Anatomy and Geometry for Deep Groupwise Image Registration
Xinzhe Luo, Xin Wang, Linda Shapiro +3
This article presents a general Bayesian learning framework for multi-modal groupwise image registration. The method builds on probabilistic modelling of the image generative proce…
IQAGPT: Image Quality Assessment with Vision-language and ChatGPT Models
Zhihao Chen, Bin Hu, Chuang Niu +4
Large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in various tasks and attracted an increasing interest as a natural language interface acros…
Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification
Qizao Wang, Xuelin Qian, Bin Li +2
Cloth-changing person Re-IDentification (Re-ID) is a particularly challenging task, suffering from two limitations of inferior discriminative features and limited training samples.…
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…
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…
Rethinking Precision of Pseudo Label: Test-Time Adaptation via Complementary Learning
Jiayi Han, Longbin Zeng, Liang Du +2
In this work, we propose a novel complementary learning approach to enhance test-time adaptation (TTA), which has been proven to exhibit good performance on testing data with distr…
RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation
Xiangyu Zhao, Zengxin Qi, Sheng Wang +4
Medical image segmentation methods are generally designed as fully-supervised to guarantee model performance, which require a significant amount of expert annotated samples that ar…
When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and A New Benchmark
Zhizhong Huang, Junping Zhang, Hongming Shan
To minimize the impact of age variation on face recognition, age-invariant face recognition (AIFR) extracts identity-related discriminative features by minimizing the correlation b…
Motion Matters: A Novel Motion Modeling For Cross-View Gait Feature Learning
Jingqi Li, Jiaqi Gao, Yuzhen Zhang +2
As a unique biometric that can be perceived at a distance, gait has broad applications in person authentication, social security, and so on. Existing gait recognition methods suffe…
Forget Less, Count Better: A Domain-Incremental Self-Distillation Learning Benchmark for Lifelong Crowd Counting
Jiaqi Gao, Jingqi Li, Hongming Shan +4
Crowd counting has important applications in public safety and pandemic control. A robust and practical crowd counting system has to be capable of continuously learning with the ne…
Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels
Yiming Lei, Haiping Zhu, Junping Zhang +1
The performance of medical image classification has been enhanced by deep convolutional neural networks (CNNs), which are typically trained with cross-entropy (CE) loss. However, w…
Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting
Jiaqi Gao, Zhizhong Huang, Yiming Lei +4
Most conventional crowd counting methods utilize a fully-supervised learning framework to establish a mapping between scene images and crowd density maps. They usually rely on a la…
SGM3D: Stereo Guided Monocular 3D Object Detection
Zheyuan Zhou, Liang Du, Xiaoqing Ye +5
Monocular 3D object detection aims to predict the object location, dimension and orientation in 3D space alongside the object category given only a monocular image. It poses a grea…
Learning Representation for Clustering via Prototype Scattering and Positive Sampling
Zhizhong Huang, Jie Chen, Junping Zhang +1
Existing deep clustering methods rely on either contrastive or non-contrastive representation learning for downstream clustering task. Contrastive-based methods thanks to negative…
Denoised Internal Models: a Brain-Inspired Autoencoder against Adversarial Attacks
Kaiyuan Liu, Xingyu Li, Yurui Lai +6
Despite its great success, deep learning severely suffers from robustness; that is, deep neural networks are very vulnerable to adversarial attacks, even the simplest ones. Inspire…
SelfGait: A Spatiotemporal Representation Learning Method for Self-supervised Gait Recognition
Yiqun Liu, Yi Zeng, Jian Pu +3
Gait recognition plays a vital role in human identification since gait is a unique biometric feature that can be perceived at a distance. Although existing gait recognition methods…
SPICE: Semantic Pseudo-labeling for Image Clustering
Chuang Niu, Hongming Shan, Ge Wang
The similarity among samples and the discrepancy between clusters are two crucial aspects of image clustering. However, current deep clustering methods suffer from the inaccurate e…
GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set
Hanqing Chao, Kun Wang, Yiwei He +2
Gait is a unique biometric feature that can be recognized at a distance; thus, it has broad applications in crime prevention, forensic identification, and social security. To portr…
RoutingGAN: Routing Age Progression and Regression with Disentangled Learning
Zhizhong Huang, Junping Zhang, Hongming Shan
Although impressive results have been achieved for age progression and regression, there remain two major issues in generative adversarial networks (GANs)-based methods: 1) conditi…