41 citations · 52 across the 10 of their papers we have counts for
10 papers
High-Energy Concentration for Federated Learning in Frequency Domain
Haozhi Shi, Weiying Xie, Hangyu Ye +4
Federated Learning (FL) presents significant potential for collaborative optimization without data sharing. Since synthetic data is sent to the server, leveraging the popular conce…
FusionSAM: Visual Multi-Modal Learning with Segment Anything
Daixun Li, Weiying Xie, Mingxiang Cao +5
Multimodal image fusion and semantic segmentation are critical for autonomous driving. Despite advancements, current models often struggle with segmenting densely packed elements d…
Reducing Spurious Correlation for Federated Domain Generalization
Shuran Ma, Weiying Xie, Daixun Li +2
The rapid development of multimedia has provided a large amount of data with different distributions for visual tasks, forming different domains. Federated Learning (FL) can effici…
E2E-MFD: Towards End-to-End Synchronous Multimodal Fusion Detection
Jiaqing Zhang, Mingxiang Cao, Weiying Xie +5
Multimodal image fusion and object detection are crucial for autonomous driving. While current methods have advanced the fusion of texture details and semantic information, their c…
Multimodal Informative ViT: Information Aggregation and Distribution for Hyperspectral and LiDAR Classification
Jiaqing Zhang, Jie Lei, Weiying Xie +3
In multimodal land cover classification (MLCC), a common challenge is the redundancy in data distribution, where irrelevant information from multiple modalities can hinder the effe…
FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients
DaiXun Li, Weiying Xie, ZiXuan Wang +3
With the rapid development of imaging sensor technology in the field of remote sensing, multi-modal remote sensing data fusion has emerged as a crucial research direction for land…
RS-DGC: Exploring Neighborhood Statistics for Dynamic Gradient Compression on Remote Sensing Image Interpretation
Weiying Xie, Zixuan Wang, Jitao Ma +2
Distributed deep learning has recently been attracting more attention in remote sensing (RS) applications due to the challenges posed by the increased amount of open data that are…
Multi-scale direction-aware SAR object detection network via global information fusion
Mingxiang Cao, Weiying Xie, Jie Lei +3
Deep learning has driven significant progress in object detection using Synthetic Aperture Radar (SAR) imagery. Existing methods, while achieving promising results, often struggle…
FedFusion: Manifold Driven Federated Learning for Multi-satellite and Multi-modality Fusion
DaiXun Li, Weiying Xie, Yunsong Li +1
Multi-satellite, multi-modality in-orbit fusion is a challenging task as it explores the fusion representation of complex high-dimensional data under limited computational resource…
MDFL: Multi-domain Diffusion-driven Feature Learning
Daixun Li, Weiying Xie, Jiaqing Zhang +1
High-dimensional images, known for their rich semantic information, are widely applied in remote sensing and other fields. The spatial information in these images reflects the obje…