123 citations · 273 across the 26 of their papers we have counts for
23 papers · 1 filter
Think and Answer ME: Benchmarking and Exploring Multi-Entity Reasoning Grounding in Remote Sensing
Shuchang Lyu, Haiquan Wen, Guangliang Cheng +5
Recent advances in reasoning language models and reinforcement learning with verifiable rewards have significantly enhanced multi-step reasoning capabilities. This progress motivat…
Deep Learning Based Domain Adaptation Methods in Remote Sensing: A Comprehensive Survey
Shuchang Lyu, Qi Zhao, Zheng Zhou +6
Domain adaptation is a crucial and increasingly important task in remote sensing, aiming to transfer knowledge from a source domain a differently distributed target domain. It has…
Adversarial Versus Federated: An Adversarial Learning based Multi-Modality Cross-Domain Federated Medical Segmentation
You Zhou, Lijiang Chen, Shuchang Lyu +7
Federated learning enables collaborative training of machine learning models among different clients while ensuring data privacy, emerging as the mainstream for breaking data silos…
RAIDX: A Retrieval-Augmented Generation and GRPO Reinforcement Learning Framework for Explainable Deepfake Detection
Tianxiao Li, Zhenglin Huang, Haiquan Wen +4
The rapid advancement of AI-generation models has enabled the creation of hyperrealistic imagery, posing ethical risks through widespread misinformation. Current deepfake detection…
Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook
Muyi Bao, Shuchang Lyu, Zhaoyang Xu +5
Deep learning has profoundly transformed remote sensing, yet prevailing architectures like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) remain constrained by…
Joint-Optimized Unsupervised Adversarial Domain Adaptation in Remote Sensing Segmentation with Prompted Foundation Model
Shuchang Lyu, Qi Zhao, Guangliang Cheng +4
Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation (UDA-RSSeg) addresses the challenge of adapting a model trained on source domain data to target domain sampl…
BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation
Zheng Zhou, Wenquan Feng, Shuchang Lyu +3
Dataset Distillation (DD) is an emerging technique that compresses large-scale datasets into significantly smaller synthesized datasets while preserving high test performance and e…
BACON: Bayesian Optimal Condensation Framework for Dataset Distillation
Zheng Zhou, Hongbo Zhao, Guangliang Cheng +4
Dataset Distillation (DD) aims to distill knowledge from extensive datasets into more compact ones while preserving performance on the test set, thereby reducing storage costs and…
OV-VG: A Benchmark for Open-Vocabulary Visual Grounding
Chunlei Wang, Wenquan Feng, Xiangtai Li +5
Open-vocabulary learning has emerged as a cutting-edge research area, particularly in light of the widespread adoption of vision-based foundational models. Its primary objective is…
Iterative Robust Visual Grounding with Masked Reference based Centerpoint Supervision
Menghao Li, Chunlei Wang, Wenquan Feng +5
Visual Grounding (VG) aims at localizing target objects from an image based on given expressions and has made significant progress with the development of detection and vision tran…
Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review
Guangliang Cheng, Yunmeng Huang, Xiangtai Li +4
Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has bro…
Local-to-Global Information Communication for Real-Time Semantic Segmentation Network Search
Guangliang Cheng, Peng Sun, Ting-Bing Xu +2
Neural Architecture Search (NAS) has shown great potentials in automatically designing neural network architectures for real-time semantic segmentation. Unlike previous works that…
Self-Training Guided Disentangled Adaptation for Cross-Domain Remote Sensing Image Semantic Segmentation
Qi Zhao, Shuchang Lyu, Binghao Liu +2
Deep convolutional neural networks (DCNNs) based remote sensing (RS) image semantic segmentation technology has achieved great success used in many real-world applications such as…
Look in Different Views: Multi-Scheme Regression Guided Cell Instance Segmentation
Menghao Li, Wenquan Feng, Shuchang Lyu +2
Cell instance segmentation is a new and challenging task aiming at joint detection and segmentation of every cell in an image. Recently, many instance segmentation methods have app…
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation
Qi Zhao, Shuchang Lyu, Wenpei Bai +7
Ovarian cancer is one of the most harmful gynecological diseases. Detecting ovarian tumors in early stage with computer-aided techniques can efficiently decrease the mortality rate…
Attention-based Feature Decomposition-Reconstruction Network for Scene Text Detection
Qi Zhao, Yufei Wang, Shuchang Lyu +1
Recently, scene text detection has been a challenging task. Texts with arbitrary shape or large aspect ratio are usually hard to detect. Previous segmentation-based methods can des…
A Feature Consistency Driven Attention Erasing Network for Fine-Grained Image Retrieval
Qi Zhao, Xu Wang, Shuchang Lyu +2
Large-scale fine-grained image retrieval has two main problems. First, low dimensional feature embedding can fasten the retrieval process but bring accuracy reduce due to overlooki…
TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios
Xingkui Zhu, Shuchang Lyu, Xu Wang +1
Object detection on drone-captured scenarios is a recent popular task. As drones always navigate in different altitudes, the object scale varies violently, which burdens the optimi…
A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation
Qi Zhao, Binghao Liu, Shuchang Lyu +1
Few-shot segmentation focuses on the generalization of models to segment unseen object with limited annotated samples. However, existing approaches still face two main challenges.…
Embedded Self-Distillation in Compact Multi-Branch Ensemble Network for Remote Sensing Scene Classification
Qi Zhao, Yujing Ma, Shuchang Lyu +1
Remote sensing (RS) image scene classification task faces many challenges due to the interference from different characteristics of different geographical elements. To solve this p…
Embedded Knowledge Distillation in Depth-Level Dynamic Neural Network
Qi Zhao, Shuchang Lyu, Zhiwei Zhang +2
In real applications, different computation-resource devices need different-depth networks (e.g., ResNet-18/34/50) with high-accuracy. Usually, existing methods either design multi…
MM-FSOD: Meta and metric integrated few-shot object detection
Yuewen Li, Wenquan Feng, Shuchang Lyu +2
In the object detection task, CNN (Convolutional neural networks) models always need a large amount of annotated examples in the training process. To reduce the dependency of expen…
MGML: Multi-Granularity Multi-Level Feature Ensemble Network for Remote Sensing Scene Classification
Qi Zhao, Shuchang Lyu, Yuewen Li +2
Remote sensing (RS) scene classification is a challenging task to predict scene categories of RS images. RS images have two main characters: large intra-class variance caused by la…