53 citations · 62 across the 13 of their papers we have counts for
13 papers
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…
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…
Unsupervised Multi-view UAV Image Geo-localization via Iterative Rendering
Haoyuan Li, Chang Xu, Wen Yang +3
Unmanned Aerial Vehicle (UAV) Cross-View Geo-Localization (CVGL) presents significant challenges due to the view discrepancy between oblique UAV images and overhead satellite image…
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…
Detecting Every Object from Events
Haitian Zhang, Chang Xu, Xinya Wang +4
Object detection is critical in autonomous driving, and it is more practical yet challenging to localize objects of unknown categories: an endeavour known as Class-Agnostic Object…
Learning Cross-view Visual Geo-localization without Ground Truth
Haoyuan Li, Chang Xu, Wen Yang +2
Cross-View Geo-Localization (CVGL) involves determining the geographical location of a query image by matching it with a corresponding GPS-tagged reference image. Current state-of-…
ConGeo: Robust Cross-view Geo-localization across Ground View Variations
Li Mi, Chang Xu, Javiera Castillo-Navarro +4
Cross-view geo-localization aims at localizing a ground-level query image by matching it to its corresponding geo-referenced aerial view. In real-world scenarios, the task requires…
Robust Tiny Object Detection in Aerial Images amidst Label Noise
Haoran Zhu, Chang Xu, Wen Yang +3
Precise detection of tiny objects in remote sensing imagery remains a significant challenge due to their limited visual information and frequent occurrence within scenes. This chal…
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…
Motion Robust High-Speed Light-Weighted Object Detection With Event Camera
Bingde Liu, Chang Xu, Wen Yang +2
In this work, we propose a motion robust and high-speed detection pipeline which better leverages the event data. First, we design an event stream representation called temporal ac…