1 citations · 1 across the 12 of their papers we have counts for
12 papers
DKDNet: Dual Knowledge and Data-Driven Network for Cross-Domain Automatic Modulation Classification
Shuang Wang, Chenxu Wang, Hantong Xing +3
The dynamics of communication environments induce significant distribution shifts across domains, challenging the generalization of deep learning-based automatic modulation classif…
TAR: Text Semantic Assisted Cross-modal Image Registration Framework for Optical and SAR Images
Zhuoyu Cai, Dou Quan, Ning Huyan +3
Existing deep learning-based methods can capture shared features from optical and synthetic aperture radar (SAR) images for spatial alignment. However, optical-SAR registration rem…
Modulation Consistency-based Contrastive Learning for Self-Supervised Automatic Modulation Classification
Chenxu Wang, Shuang Wang, Lirong Han +4
Deep learning-based AMC methods have achieved remarkable performance, but their practical deployment remains constrained by the high cost of labeled data. Although self-supervised…
BGG: Bridging the Geometric Gap between Cross-View images by Vision Foundation Model Adaptation for Geo-Localization
Wei Wang, Dou Quan, Ning Huyan +4
Geometric differences between cross-view images, such as drone and satellite views, significantly increase the challenge of Cross-View Geo-Localization (CVGL), which aims to acquir…
CLNet: Cross-View Correspondence Makes a Stronger Geo-Localizationer
Xianwei Cao, Dou Quan, Shuang Wang +4
Image retrieval-based cross-view geo-localization (IRCVGL) aims to match images captured from significantly different viewpoints, such as satellite and street-level images. Existin…
Multi-Expert Learning Framework with the State Space Model for Optical and SAR Image Registration
Wei Wang, Dou Quan, Ning Huyan +4
Optical and Synthetic Aperture Radar (SAR) image registration is crucial for multi-modal image fusion and applications. However, several challenges limit the performance of existin…
Lightweight Adapter Learning for More Generalized Remote Sensing Change Detection
Dou Quan, Rufan Zhou, Shuang Wang +4
Deep learning methods have shown promising performances in remote sensing image change detection (CD). However, existing methods usually train a dataset-specific deep network for e…
Generalization-aware Remote Sensing Change Detection via Domain-agnostic Learning
Qi Zang, Shuang Wang, Dong Zhao +3
Change detection has essential significance for the region's development, in which pseudo-changes between bitemporal images induced by imaging environmental factors are key challen…
Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation
Dong Zhao, Shuang Wang, Qi Zang +3
We study source-free unsupervised domain adaptation (SFUDA) for semantic segmentation, which aims to adapt a source-trained model to the target domain without accessing the source…
Transcending Fusion: A Multi-Scale Alignment Method for Remote Sensing Image-Text Retrieval
Rui Yang, Shuang Wang, Yingping Han +5
Remote Sensing Image-Text Retrieval (RSITR) is pivotal for knowledge services and data mining in the remote sensing (RS) domain. Considering the multi-scale representations in imag…
Transferring Modality-Aware Pedestrian Attentive Learning for Visible-Infrared Person Re-identification
Yuwei Guo, Wenhao Zhang, Licheng Jiao +3
Visible-infrared person re-identification (VI-ReID) aims to search the same pedestrian of interest across visible and infrared modalities. Existing models mainly focus on compensat…
Semantic Connectivity-Driven Pseudo-labeling for Cross-domain Segmentation
Dong Zhao, Ruizhi Yang, Shuang Wang +5
Presently, self-training stands as a prevailing approach in cross-domain semantic segmentation, enhancing model efficacy by training with pixels assigned with reliable pseudo-label…