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20232026
most citedRobust Tiny Object Detection in Aerial Images amidst Label Noise

5 citations · 9 across the 12 of their papers we have counts for

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12 papers · 1 filter

cs.CV2026

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…

cs.CV2025★ 1 cited

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…

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…