5 citations · 7 across the 6 of their papers we have counts for
6 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…
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…
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…
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…