1 code implementation • 6 Feb 2023 • Qinrou Wen, Jirui Yang, Xue Yang, Kewei Liang
To further refine masks obtained by compressed vectors, we propose for the first time a compressed vector based multi-stage refinement framework.
3 code implementations • 29 Jan 2022 • Xue Yang, Yue Zhou, Gefan Zhang, Jirui Yang, Wentao Wang, Junchi Yan, Xiaopeng Zhang, Qi Tian
This is in contrast to recent Gaussian modeling based rotation detectors e. g. GWD loss and KLD loss that involve a human-specified distribution distance metric which require additional hyperparameter tuning that vary across datasets and detectors.
2 code implementations • NeurIPS 2021 • Xue Yang, Xiaojiang Yang, Jirui Yang, Qi Ming, Wentao Wang, Qi Tian, Junchi Yan
Taking the perspective that horizontal detection is a special case for rotated object detection, in this paper, we are motivated to change the design of rotation regression loss from induction paradigm to deduction methodology, in terms of the relation between rotation and horizontal detection.
Ranked #14 on Object Detection In Aerial Images on DOTA (using extra training data)
1 code implementation • CVPR 2021 • Xing Shen, Jirui Yang, Chunbo Wei, Bing Deng, Jianqiang Huang, Xiansheng Hua, Xiaoliang Cheng, Kewei Liang
Generally, a low-resolution grid is not sufficient to capture the details, while a high-resolution grid dramatically increases the training complexity.
3 code implementations • ICCV 2019 • Xue Yang, Jirui Yang, Junchi Yan, Yue Zhang, Tengfei Zhang, Zhi Guo, Sun Xian, Kun fu
Specifically, a sampling fusion network is devised which fuses multi-layer feature with effective anchor sampling, to improve the sensitivity to small objects.
Ranked #47 on Object Detection In Aerial Images on DOTA (using extra training data)
4 code implementations • 12 Jun 2018 • Xue Yang, Hao Sun, Kun fu, Jirui Yang, Xian Sun, Menglong Yan, Zhi Guo
Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall.