29 citations · 33 across the 15 of their papers we have counts for
16 papers
Hierarchical Fine-Grained Aerial Object Detection
Yan Zhang, Fang Xu, Wen Yang +1
Fine-grained aerial object detection, driven by the intrinsic granularity of real-world object categories, is crucial for advanced scene understanding in remote sensing. Existing m…
UHR-DETR: Efficient End-to-End Small Object Detection for Ultra-High-Resolution Remote Sensing Imagery
Jingfang Li, Haoran Zhu, Wen Yang +4
Ultra-High-Resolution (UHR) imagery has become essential for modern remote sensing, offering unprecedented spatial coverage. However, detecting small objects in such vast scenes pr…
Generalized Small Object Detection:A Point-Prompted Paradigm and Benchmark
Haoran Zhu, Wen Yang, Guangyou Yang +5
Small object detection (SOD) remains challenging due to extremely limited pixels and ambiguous object boundaries. These characteristics lead to challenging annotation, limited avai…
Unifying UAV Cross-View Geo-Localization via 3D Geometric Perception
Haoyuan Li, Wen Yang, Fang Xu +4
Cross-view geo-localization for Unmanned Aerial Vehicles (UAVs) operating in GNSS-denied environments remains challenging due to the severe geometric discrepancy between oblique UA…
Mask Clustering-based Annotation Engine for Large-Scale Submeter Land Cover Mapping
Hao Chen, Fang Xu, Tamer Saleh +2
Recent advances in remote sensing technology have made submeter resolution imagery increasingly accessible, offering remarkable detail for fine-grained land cover analysis. However…
Mitigating the Impact of Prominent Position Shift in Drone-based RGBT Object Detection
Yan Zhang, Wen Yang, Chang Xu +3
Drone-based RGBT object detection plays a crucial role in many around-the-clock applications. However, real-world drone-viewed RGBT data suffers from the prominent position shift p…
Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning
Chang Xu, Ruixiang Zhang, Wen Yang +4
Detecting oriented tiny objects, which are limited in appearance information yet prevalent in real-world applications, remains an intricate and under-explored problem. To address t…
Tiny Object Detection with Single Point Supervision
Haoran Zhu, Chang Xu, Ruixiang Zhang +4
Tiny objects, with their limited spatial resolution, often resemble point-like distributions. As a result, bounding box prediction using point-level supervision emerges as a natura…
Frequency-Adaptive Low-Latency Object Detection Using Events and Frames
Haitian Zhang, Xiangyuan Wang, Chang Xu +5
Fusing Events and RGB images for object detection leverages the robustness of Event cameras in adverse environments and the rich semantic information provided by RGB cameras. Howev…
LaVIDE: Language-Prompted Satellite Change Detection via Map-Image Alignment
Shuguo Jiang, Fang Xu, Chuandong Liu +6
Remote sensing change detection based on a map reference and an up-to-date image boosts timely observation of the Earth's surface when earlier images are lacking for comparison. Ho…
Enhancing Fine-grained Object Detection in Aerial Images via Orthogonal Mapping
Haoran Zhu, Yifan Zhou, Chang Xu +2
Fine-Grained Object Detection (FGOD) is a critical task in high-resolution aerial image analysis. This letter introduces Orthogonal Mapping (OM), a simple yet effective method aime…
Rethinking Scale Imbalance in Semi-supervised Object Detection for Aerial Images
Ruixiang Zhang, Chang Xu, Fang Xu +4
This paper focuses on the scale imbalance problem of semi-supervised object detection(SSOD) in aerial images. Compared to natural images, objects in aerial images show smaller size…
Multi-Modal and Multi-Resolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark
Fang Xu, Yilei Shi, Patrick Ebel +2
Cloud removal is a significant and challenging problem in remote sensing, and in recent years, there have been notable advancements in this area. However, two major issues remain h…
GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion
Fang Xu, Yilei Shi, Patrick Ebel +4
The challenge of the cloud removal task can be alleviated with the aid of Synthetic Aperture Radar (SAR) images that can penetrate cloud cover. However, the large domain gap betwee…
Motion Deblurring with Real Events
Fang Xu, Lei Yu, Bishan Wang +5
In this paper, we propose an end-to-end learning framework for event-based motion deblurring in a self-supervised manner, where real-world events are exploited to alleviate the per…
Matching Neuromorphic Events and Color Images via Adversarial Learning
Fang Xu, Shijie Lin, Wen Yang +3
The event camera has appealing properties: high dynamic range, low latency, low power consumption and low memory usage, and thus provides complementariness to conventional frame-ba…