79 citations · 341 across the 40 of their papers we have counts for
40 papers
Learning Spectral-Decomposed Tokens for Domain Generalized Semantic Segmentation
Jingjun Yi, Qi Bi, Hao Zheng +5
The rapid development of Vision Foundation Model (VFM) brings inherent out-domain generalization for a variety of down-stream tasks. Among them, domain generalized semantic segment…
Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing
Zhe Kong, Wentian Zhang, Tao Wang +4
Face recognition systems have raised concerns due to their vulnerability to different presentation attacks, and system security has become an increasingly critical concern. Althoug…
Adversarial Medical Image with Hierarchical Feature Hiding
Qingsong Yao, Zecheng He, Yuexiang Li +4
Deep learning based methods for medical images can be easily compromised by adversarial examples (AEs), posing a great security flaw in clinical decision-making. It has been discov…
Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views
Dong Wei, Yawen Huang, Donghuan Lu +2
Background: View planning for the acquisition of cardiac magnetic resonance (CMR) imaging remains a demanding task in clinical practice. Purpose: Existing approaches to its automat…
BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion
Jinheng Xie, Yuexiang Li, Yawen Huang +4
Recent text-to-image diffusion models have demonstrated an astonishing capacity to generate high-quality images. However, researchers mainly studied the way of synthesizing images…
MEPNet: A Model-Driven Equivariant Proximal Network for Joint Sparse-View Reconstruction and Metal Artifact Reduction in CT Images
Hong Wang, Minghao Zhou, Dong Wei +2
Sparse-view computed tomography (CT) has been adopted as an important technique for speeding up data acquisition and decreasing radiation dose. However, due to the lack of sufficie…
Cross-Modal Vertical Federated Learning for MRI Reconstruction
Yunlu Yan, Hong Wang, Yawen Huang +5
Federated learning enables multiple hospitals to cooperatively learn a shared model without privacy disclosure. Existing methods often take a common assumption that the data from d…
Dynamically Masked Discriminator for Generative Adversarial Networks
Wentian Zhang, Haozhe Liu, Bing Li +5
Training Generative Adversarial Networks (GANs) remains a challenging problem. The discriminator trains the generator by learning the distribution of real/generated data. However,…
VisorGPT: Learning Visual Prior via Generative Pre-Training
Jinheng Xie, Kai Ye, Yudong Li +5
Various stuff and things in visual data possess specific traits, which can be learned by deep neural networks and are implicitly represented as the visual prior, e.g., object locat…
Open-World Weakly-Supervised Object Localization
Jinheng Xie, Zhaochuan Luo, Yuexiang Li +3
While remarkable success has been achieved in weakly-supervised object localization (WSOL), current frameworks are not capable of locating objects of novel categories in open-world…
Improving GAN Training via Feature Space Shrinkage
Haozhe Liu, Wentian Zhang, Bing Li +6
Due to the outstanding capability for data generation, Generative Adversarial Networks (GANs) have attracted considerable attention in unsupervised learning. However, training GANs…
Interactive Segmentation as Gaussian Process Classification
Minghao Zhou, Hong Wang, Qian Zhao +4
Click-based interactive segmentation (IS) aims to extract the target objects under user interaction. For this task, most of the current deep learning (DL)-based methods mainly foll…
A New Perspective to Boost Vision Transformer for Medical Image Classification
Yuexiang Li, Yawen Huang, Nanjun He +2
Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale lab…
Orientation-Shared Convolution Representation for CT Metal Artifact Learning
Hong Wang, Qi Xie, Yuexiang Li +3
During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts in the captured CT images and then impair the clinical trea…
Decoupled Mixup for Generalized Visual Recognition
Haozhe Liu, Wentian Zhang, Jinheng Xie +7
Convolutional neural networks (CNN) have demonstrated remarkable performance when the training and testing data are from the same distribution. However, such trained CNN models oft…
A Benchmark for Weakly Semi-Supervised Abnormality Localization in Chest X-Rays
Haoqin Ji, Haozhe Liu, Yuexiang Li +7
Accurate abnormality localization in chest X-rays (CXR) can benefit the clinical diagnosis of various thoracic diseases. However, the lesion-level annotation can only be performed…
Combating Mode Collapse in GANs via Manifold Entropy Estimation
Haozhe Liu, Bing Li, Haoqian Wu +5
Generative Adversarial Networks (GANs) have shown compelling results in various tasks and applications in recent years. However, mode collapse remains a critical problem in GANs. I…
Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction
Hong Wang, Yuexiang Li, Deyu Meng +1
Inspired by the great success of deep neural networks, learning-based methods have gained promising performances for metal artifact reduction (MAR) in computed tomography (CT) imag…
mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation
Yao Zhang, Nanjun He, Jiawei Yang +6
Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) is desirable to joint learning of multimodal images. However, in clinical practice, it is not always possibl…
Robust Representation via Dynamic Feature Aggregation
Haozhe Liu, Haoqin Ji, Yuexiang Li +5
Deep convolutional neural network (CNN) based models are vulnerable to the adversarial attacks. One of the possible reasons is that the embedding space of CNN based model is sparse…
DFTR: Depth-supervised Fusion Transformer for Salient Object Detection
Heqin Zhu, Xu Sun, Yuexiang Li +3
Automated salient object detection (SOD) plays an increasingly crucial role in many computer vision applications. By reformulating the depth information as supervision rather than…
Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images
Hong Liu, Dong Wei, Donghuan Lu +4
Automated surface segmentation of retinal layer is important and challenging in analyzing optical coherence tomography (OCT). Recently, many deep learning based methods have been d…
Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training
Yi Lin, Zhiyong Qu, Hao Chen +6
Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology. Generally, the segmentation performance of fully-supervised learning heavily depends on t…
Relational Experience Replay: Continual Learning by Adaptively Tuning Task-wise Relationship
Quanziang Wang, Renzhen Wang, Yuexiang Li +4
Continual learning is a promising machine learning paradigm to learn new tasks while retaining previously learned knowledge over streaming training data. Till now, rehearsal-based…