Z
Zhen Zhu
Researcher at Huazhong University of Science and Technology
Publications - 22
Citations - 3635
Zhen Zhu is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Object detection & Minimum bounding box. The author has an hindex of 13, co-authored 22 publications receiving 1685 citations. Previous affiliations of Zhen Zhu include Wuhan University.
Papers
More filters
Proceedings ArticleDOI
DOTA: A Large-Scale Dataset for Object Detection in Aerial Images
Gui-Song Xia,Xiang Bai,Jian Ding,Zhen Zhu,Serge Belongie,Jiebo Luo,Mihai Datcu,Marcello Pelillo,Liangpei Zhang +8 more
TL;DR: The Dataset for Object Detection in Aerial Images (DOTA) as discussed by the authors is a large-scale dataset of aerial images collected from different sensors and platforms and contains objects exhibiting a wide variety of scales, orientations, and shapes.
Proceedings ArticleDOI
Rotation-Sensitive Regression for Oriented Scene Text Detection
TL;DR: The proposed method named Rotation-sensitive Regression Detector (RRD) achieves state-of-the-art performance on several oriented scene text benchmark datasets, including ICDAR 2015, MSRA-TD500, RCTW-17, and COCO-Text, and achieves a significant improvement on a ship collection dataset, demonstrating its generality on oriented object detection.
Proceedings ArticleDOI
Asymmetric Non-Local Neural Networks for Semantic Segmentation
TL;DR: Asymmetric pyramid non-local block (APNB) as mentioned in this paper is proposed to fuse the features of different levels under a sufficient consideration of long range dependencies and thus considerably improves the performance.
Posted Content
Asymmetric Non-local Neural Networks for Semantic Segmentation
TL;DR: Asymmetric Pyramid Non- local Block (APNB) and Asymmetric Fusion Non-local Block (AFNB) are presented, which has two prominent components: APNB leverages a pyramid sampling module into the non-local block to largely reduce the computation and memory consumption without sacrificing the performance.
Proceedings ArticleDOI
Progressive Pose Attention Transfer for Person Image Generation
TL;DR: Zhang et al. as mentioned in this paper proposed a new generative adversarial network to the problem of pose transfer, i.e., transferring the pose of a given person to a target one.