Author details
Author's Published Works
Scheduled Maintenance on Monday 1/13/2025
Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.
Author details
Showing 1-25 of 394 results
- Sort
Filter Results
Show
Sort By
Results
Cross-modal Gaussian Localization Distillation for Optical Information guided SAR Object Detection
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Synthetic Aperture Radar (SAR) images contain a dense clutter of objects that can be better characterized using bounding boxes with angles. However, accurately detecting the angles of objects remains challenging due to the imaging mechanism of SAR. To address this issue, we propose a novel knowledge distillation method called cross-modal Gaussian Localization Distillation (GaLD). It aims to improv...Show More
MoCoLSK: Modality-Conditioned High-Resolution Downscaling for Land Surface Temperature
Qun Dai;Chunyang Yuan;Yimian Dai;Yuxuan Li;Xiang Li;Kang Ni;Jianhui Xu;Xiangbo Shu;Jian Yang
Land surface temperature (LST) is a critical parameter for environmental studies, but directly obtaining high spatial resolution LST data remains challenging due to the spatiotemporal tradeoff in satellite remote sensing. Guided LST downscaling has emerged as an alternative solution to overcome these limitations, but current methods often neglect spatial nonstationarity, and there is a lack of an ...Show More
Weakly Supervised Object Localization With Progressive Activation Diffusion
IEEE Transactions on Neural Networks and Learning Systems
Year: 2025 | Early Access Article |
Weakly supervised object localization (WSOL) aims to locate objects with only image-level labels. Previous works mainly follow the framework of class activation map (CAM), which discovers the objects by estimating the contribution of each pixel position to the category prediction. However, most of them overlook the pixel-level spatial and semantic contextual correlation, resulting in: 1) limited a...Show More
Class-Agnostic Counting (CAC) aims to count object instances in an image by simply specifying a few exemplar boxes of interest. The key challenge for CAC is how to tailor a desirable interaction between exemplar and query features. Previous CAC methods implement such interaction by solely leveraging standard global feature convolution. We find this interaction leads to under-match caused by intra-...Show More
Enhanced Acceleration for Generalized Nonconvex Low-Rank Matrix Learning
Hengmin Zhang;Jian Yang;Wenli Du;Bob Zhang;Zhiyuan Zha;Bihan Wen
Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, o...Show More
A Collaborative Service Composition Approach Considering Providers’ Self-Interest and Minimal Service Sharing
Service composition dynamically integrates various services from multiple providers to meet complex user requirements. However, most existing methods assume centralized control over all services, which is often unrealistic because providers typically prefer to independently manage their own services, posing challenges to the application of traditional methods. Collaborative service composition off...Show More
Metric Learning-Based Subspace Clustering
IEEE Transactions on Neural Networks and Learning Systems
Year: 2025 | Early Access Article |
The self-expressive strategy has shown excellent capabilities in realizing low-dimensional representations of high-dimensional data for subspace clustering algorithms. The existing designs, however, are formulated on the linearization assumptions of the data, neglecting the precise characterization of linear relationships within samples. Considering that real-world data adheres to diverse distribu...Show More
TRTST: Arbitrary High-Quality Text-Guided Style Transfer With Transformers
Text-guided style transfer aims to repaint a content image with the target style described by a text prompt, offering greater flexibility and creativity compared to traditional image-guided style transfer. Despite the potential, existing text-guided style transfer methods often suffer from many issues, including insufficient visual quality, poor generalization ability, or a reliance on large amoun...Show More
Learning the Optimal Discriminant SVM With Feature Extraction
Subspace learning and Support Vector Machine (SVM) are two critical techniques in pattern recognition, playing pivotal roles in feature extraction and classification. However, how to learn the optimal subspace such that the SVM classifier can perform the best is still a challenging problem due to the difficulty in optimization, computation, and algorithm convergence. To address these problems, thi...Show More
Geometry-Based Cooperative Conflict Resolution for Multi-UAV Combining Heading and Speed Control
IEEE Transactions on Consumer Electronics
Year: 2025 | Early Access Article |
A safe and efficient conflict resolution method for Unmanned Aerial Vehicles (UAVs) is essential for the safe operation of multi-UAV systems in complex environments. This paper proposes a geometry-based decentralized cooperative conflict resolution method. Firstly, the safe separation constraints for pairwise conflicts are analyzed, and the linearization of constraints is achieved by using the spa...Show More
A Deep Reinforcement Learning Method for Collision Avoidance with Dense Speed-Constrained Multi-UAV
This letter introduces a novel deep reinforcement learning (DRL) method for collision avoidance problem of fixed-wing unmanned aerial vehicles (UAVs). First, with considering the characteristics of collision avoidance problem, a collision prediction method is proposed to identify the neighboring UAVs with a significant threat. A convolutional neural network model is devised to extract the dynamic ...Show More
Edge-Enhanced Heterogeneous Graph Transformer With Priority-Based Feature Aggregation for Multi-Agent Trajectory Prediction
Trajectory prediction, which aims to predict the future positions of all agents in a crowd scene, given their past trajectories, plays a vital role in improving the safety of autonomous driving vehicles. For heterogeneous agents, it is imperative to account for the gap in feature distribution differences between agents in different categories. Besides, exploring the reference relationship between ...Show More
Fine-Grained Visual Text Prompting
Vision-Language Models (VLMs), such as CLIP, excel in zero-shot image-level visual understanding but struggle with object-based tasks requiring precise localization and recognition. Visual prompts, like colorful boxes or circles, are suggested to enhance local perception. However, these methods often include irrelevant and noisy pixels, leading to suboptimal performance. The design of better visua...Show More
Rethinking Unsupervised Graph Anomaly Detection With Deep Learning: Residuals and Objectives
Xiaoxiao Ma;Fanzhen Liu;Jia Wu;Jian Yang;Shan Xue;Quan Z. Sheng
Anomalies often occur in real-world information networks/graphs, such as malevolent users in online review networks and fake news in social media. When representing such structured network data as graphs, anomalies usually appear as anomalous nodes that exhibit significantly deviated structure patterns, or different attributes, or the both. To date, numerous unsupervised methods have been develope...Show More
Dual Prototype Contrastive Network for Generalized Zero-Shot Learning
Huajie Jiang;Zhengxian Li;Yongli Hu;Baocai Yin;Jian Yang;Anton van den Hengel;Ming-Hsuan Yang;Yuankai Qi
Generalized zero-shot learning (GZSL) requires that models are able to recognize classes they were trained on, and new classes they haven't seen before. Feature-generation approaches are popular due to their effectiveness in mitigating overfitting to the training classes. Existing generative approaches usually adopt simple discriminators for distribution or classification supervision, however, thu...Show More
C3R: A Novel Classification Model-Based Coordination Method for Online Conflict Resolution of Multiple Unmanned Aerial Vehicles
Jian Yang;Fan Yang;Xiao Xu;Kaixin Zhang;Xiangke Wang;Guoli Yang
The wide applications of Unmanned Aerial Vehicles (UAVs) in the low altitude urban airspace require the elaborate airspace conflict resolution mechanism. The online decentralized coordination method is the essential part of this mechanism. This paper proposes a novel classification model-based coordination method. Firstly, the collision cone-based safe separation constraints are studied and the de...Show More
A Survey on Truth Discovery: Concepts, Methods, Applications, and Opportunities
Shuang Wang;He Zhang;Quan Z. Sheng;Xiaoping Li;Zhu Sun;Taotao Cai;Wei Emma Zhang;Jian Yang;Qing Gao
In the era of data information explosion, there are different observations on an object (e.g., the height of the Himalayas) from different sources on the web, social sensing, crowd sensing, and data sensing applications. Observations from different sources on an object can conflict with each other due to errors, missing records, typos, outdated data, etc. How to discover truth facts for objects fr...Show More
Heterogeneous Social Event Detection via Hyperbolic Graph Representations
Social events reflect the dynamics of society and, here, natural disasters and emergencies receive significant attention. The timely detection of these events can provide organisations and individuals with valuable information to reduce or avoid losses. However, due to the complex heterogeneities of the content and structure of social media, existing models can only learn limited information; larg...Show More
Knowledge Distillation Guided Interpretable Brain Subgraph Neural Networks for Brain Disorder Exploration
Xuexiong Luo;Jia Wu;Jian Yang;Hongyang Chen;Zhao Li;Hao Peng;Chuan Zhou
The human brain is a highly complex neurological system that has been the subject of continuous exploration by scientists. With the help of modern neuroimaging techniques, there has been significant progress made in brain disorder analysis. There is an increasing interest about utilizing artificial intelligence techniques to improve the efficiency of disorder diagnosis in recent years. However, th...Show More
Point Cloud Registration-Driven Robust Feature Matching for 3-D Siamese Object Tracking
Haobo Jiang;Kaihao Lan;Le Hui;Guangyu Li;Jin Xie;Shangbing Gao;Jian Yang
Learning robust feature matching between the template and search area is crucial for 3-D Siamese tracking. The core of Siamese feature matching is how to assign high feature similarity to the corresponding points between the template and the search area for precise object localization. In this article, we propose a novel point cloud registration-driven Siamese tracking framework, with the intuitio...Show More
DONet: Dual-Octave Network for Fast MR Image Reconstruction
Chun-Mei Feng;Zhanyuan Yang;Huazhu Fu;Yong Xu;Jian Yang;Ling Shao
Magnetic resonance (MR) image acquisition is an inherently prolonged process, whose acceleration has long been the subject of research. This is commonly achieved by obtaining multiple undersampled images, simultaneously, through parallel imaging. In this article, we propose the dual-octave network (DONet), which is capable of learning multiscale spatial-frequency features from both the real and im...Show More
TROPICAL: Transformer-Based Hypergraph Learning for Camouflaged Fraudster Detection
Venus Haghighi;Behnaz Soltani;Nasrin Shabani;Jia Wu;Yang Zhang;Lina Yao;Quan Z. Sheng;Jian Yang
2024 IEEE International Conference on Data Mining (ICDM)
Year: 2024 | Conference Paper |
Graph-based fraud detection has attracted increasing attention in recent years, reflecting its growing potential in mitigating sophisticated fraudulent activities. The main objective of graph-based fraud detection is to discern between fraud-sters and normal entities within graphs. As fraudsters adopt increasingly sophisticated camouflage tactics, combating them has become an urgent task. Despite ...Show More
Debunking Fake News in Online Social Networks Without Text Analysis
2024 IEEE International Conference on Data Mining (ICDM)
Year: 2024 | Conference Paper |
Since the inception of online fake news detection, the technique of natural language processing has predominantly been leading the field by utilizing text classification to discern veracity. From the network perspective, news traveling within a social network typically exhibits non-textual correlations aligned with the network of news propagation or news-user interaction. Therefore, with the advan...Show More
Counterfactual Brain Graph Augmentation Guided Bi-Level Contrastive Learning for Disorder Analysis
Guangwei Dong;Xuexiong Luo;Jing Du;Jia Wu;Shan Xue;Jian Yang;Amin Beheshti
2024 IEEE International Conference on Data Mining (ICDM)
Year: 2024 | Conference Paper |
Deep learning is increasingly crucial in scientific discovery, accelerating research in various fields. Exploring brain science using deep learning has garnered significant interest, particularly in the recognition of brain disorders. However, existing methods face limitations in the discriminability of learned brain graph representations and the identification of neurological biomarkers associate...Show More
Predictive Maneuver for UAV Air Combat Confrontation Based on Game Theory
2024 China Automation Congress (CAC)
Year: 2024 | Conference Paper |
This paper presents a game theory-based decision-making algorithm for air combat maneuvers. The Dubins minimum achievable distance is introduced to replace the Euclidean distance, and situation assessment function is reconstructed as the utility function of the game model, providing a more accurate basis for maneuver decisions. To effectively handle different opponents UAVs, their autonomous maneu...Show More
IEEE Account
Purchase Details
Profile Information
Need Help?
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Contact Us
- Help
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Sitemap
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.