150 citations · 583 across the 49 of their papers we have counts for
49 papers
Position-Aware Self-supervised Representation Learning for Cross-mode Radar Signal Recognition
Hongyang Zhang, Haitao Zhang, Yinhao Liu +3
Radar signal recognition in open electromagnetic environments is challenging due to diverse operating modes and unseen radar types. Existing methods often overlook position relatio…
Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly Detection
Jiaxiang Wang, Haote Xu, Xiaolu Chen +4
Anomaly detection (AD) in 3D point clouds is crucial in a wide range of industrial applications, especially in various forms of precision manufacturing. Considering the industrial…
Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors
Yunlong Lin, Zhenqi Fu, Kairun Wen +7
Low-light image enhancement (LIE) aims at precisely and efficiently recovering an image degraded in poor illumination environments. Recent advanced LIE techniques are using deep ne…
Unity in Diversity: Multi-expert Knowledge Confrontation and Collaboration for Generalizable Vehicle Re-identification
Zhenyu Kuang, Hongyang Zhang, Mang Ye +5
Generalizable vehicle re-identification (ReID) seeks to develop models that can adapt to unknown target domains without the need for additional fine-tuning or retraining. Previous…
SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detection
Zhijie Rao, Jingcai Guo, Luyao Tang +3
This paper provides a novel framework for single-domain generalized object detection (i.e., Single-DGOD), where we are interested in learning and maintaining the semantic structure…
Learning an Interpretable End-to-End Network for Real-Time Acoustic Beamforming
Hao Liang, Guanxing Zhou, Xiaotong Tu +3
Recently, many forms of audio industrial applications, such as sound monitoring and source localization, have begun exploiting smart multi-modal devices equipped with a microphone…
Hint-dynamic Knowledge Distillation
Yiyang Liu, Chenxin Li, Xiaotong Tu +2
Knowledge Distillation (KD) transfers the knowledge from a high-capacity teacher model to promote a smaller student model. Existing efforts guide the distillation by matching their…
Uncertainty Inspired Underwater Image Enhancement
Zhenqi Fu, Wu Wang, Yue Huang +2
A main challenge faced in the deep learning-based Underwater Image Enhancement (UIE) is that the ground truth high-quality image is unavailable. Most of the existing methods first…
Knowledge Condensation Distillation
Chenxin Li, Mingbao Lin, Zhiyuan Ding +5
Knowledge Distillation (KD) transfers the knowledge from a high-capacity teacher network to strengthen a smaller student. Existing methods focus on excavating the knowledge hints a…
Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection
Chaoqi Chen, Jiongcheng Li, Hong-Yu Zhou +4
Domain Adaptive Object Detection (DAOD) focuses on improving the generalization ability of object detectors via knowledge transfer. Recent advances in DAOD strive to change the emp…
A Closer Look at Personalization in Federated Image Classification
Changxing Jing, Yan Huang, Yihong Zhuang +4
Federated Learning (FL) is developed to learn a single global model across the decentralized data, while is susceptible when realizing client-specific personalization in the presen…
AFSC: Adaptive Fourier Space Compression for Anomaly Detection
Haote Xu, Yunlong Zhang, Liyan Sun +3
Anomaly Detection (AD) on medical images enables a model to recognize any type of anomaly pattern without lesion-specific supervised learning. Data augmentation based methods const…
Acoustic-Net: A Novel Neural Network for Sound Localization and Quantification
Guanxing Zhou, Hao Liang, Xinghao Ding +3
Acoustic source localization has been applied in different fields, such as aeronautics and ocean science, generally using multiple microphones array data to reconstruct the source…
Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis
Yunlong Zhang, Xin Lin, Yihong Zhuang +6
Synthesizing a subject-specific pathology-free image from a pathological image is valuable for algorithm development and clinical practice. In recent years, several approaches base…
GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges
Junde Wu, Huihui Fang, Fei Li +26
Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both two modalities of images have prominent biomarker…
Self-Verification in Image Denoising
Huangxing Lin, Yihong Zhuang, Delu Zeng +3
We devise a new regularization, called self-verification, for image denoising. This regularization is formulated using a deep image prior learned by the network, rather than a trad…
Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation
Chenxin Li, Qi Qi, Xinghao Ding +3
Medical imaging datasets usually exhibit domain shift due to the variations of scanner vendors, imaging protocols, etc. This raises the concern about the generalization capacity of…
Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation
Chenxin Li, Wenao Ma, Liyan Sun +4
The analysis of organ vessels is essential for computer-aided diagnosis and surgical planning. But it is not a easy task since the fine-detailed connected regions of organ vessel b…
Self-Regression Learning for Blind Hyperspectral Image Fusion Without Label
Wu Wang, Yue Huang, Xinhao Ding
Hyperspectral image fusion (HIF) is critical to a wide range of applications in remote sensing and many computer vision applications. Most traditional HIF methods assume that the o…
I3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors
Chaoqi Chen, Zebiao Zheng, Yue Huang +2
Recent works on two-stage cross-domain detection have widely explored the local feature patterns to achieve more accurate adaptation results. These methods heavily rely on the regi…
Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translation
Chenxin Li, Yunlong Zhang, Jiongcheng Li +2
The goal of unsupervised anomaly segmentation (UAS) is to detect the pixel-level anomalies unseen during training. It is a promising field in the medical imaging community, e.g, we…
Consistent Posterior Distributions under Vessel-Mixing: A Regularization for Cross-Domain Retinal Artery/Vein Classification
Chenxin Li, Yunlong Zhang, Zhehan Liang +3
Retinal artery/vein (A/V) classification is a critical technique for diagnosing diabetes and cardiovascular diseases. Although deep learning based methods achieve impressive result…
Underwater Image Enhancement via Learning Water Type Desensitized Representations
Zhenqi Fu, Xiaopeng Lin, Wu Wang +2
We present a novel underwater image enhancement method termed SCNet to improve the image quality meanwhile cope with the degradation diversity caused by the water. SCNet is based o…
Twice Mixing: A Rank Learning based Quality Assessment Approach for Underwater Image Enhancement
Zhenqi Fu, Xueyang Fu, Yue Huang +1
To improve the quality of underwater images, various kinds of underwater image enhancement (UIE) operators have been proposed during the past few years. However, the lack of effect…