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20182026
most citedDetecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark

415 citations · 762 across the 49 of their papers we have counts for

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Showing 2024 · cs.CVShow all

10 papers · 2 filters

cs.CV2024★ 1 cited

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…

cs.CV2024★ 1 cited

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…

cs.CV2024★ 1 cited

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…