Q
Qishang Cheng
Researcher at University of Electronic Science and Technology of China
Publications - 7
Citations - 263
Qishang Cheng is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Object detection & Artificial neural network. The author has an hindex of 4, co-authored 7 publications receiving 108 citations.
Papers
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Book ChapterDOI
VisDrone-DET2018: The Vision Meets Drone Object Detection in Image Challenge Results
Pengfei Zhu,Longyin Wen,Dawei Du,Xiao Bian,Haibin Ling,Qinghua Hu,Qinqin Nie,Hao Cheng,Chenfeng Liu,Xiaoyu Liu,Wenya Ma,Haotian Wu,Lianjie Wang,Arne Schumann,Chase Brown,Chen Qian,Chengzheng Li,Dongdong Li,Emmanouil Michail,Fan Zhang,Feng Ni,Feng Zhu,Guanghui Wang,Haipeng Zhang,Han Deng,Hao Liu,Haoran Wang,Heqian Qiu,Honggang Qi,Honghui Shi,Hongliang Li,Hongyu Xu,Hu Lin,Ioannis Kompatsiaris,Jian Cheng,Jianqiang Wang,Jianxiu Yang,Jingkai Zhou,Juanping Zhao,K J Joseph,Kaiwen Duan,Karthik Suresh,Bo Ke,Ke Wang,Konstantinos Avgerinakis,Lars Sommer,Lei Zhang,Li Yang,Lin Cheng,Lin Ma,Liyu Lu,Lu Ding,Minyu Huang,Naveen Kumar Vedurupaka,Nehal Mamgain,Nitin Bansal,Oliver Acatay,Panagiotis Giannakeris,Qian Wang,Qijie Zhao,Qingming Huang,Qiong Liu,Qishang Cheng,Qiuchen Sun,Robert Laganiere,Sheng Jiang,Shengjin Wang,Shubo Wei,Siwei Wang,Stefanos Vrochidis,Sujuan Wang,Tiaojio Lee,Usman Sajid,Vineeth N Balasubramanian,Wei Li,Wei Zhang,Weikun Wu,Wenchi Ma,Wenrui He,Wenzhe Yang,Xiaoyu Chen,Xin Sun,Xinbin Luo,Xintao Lian,Xiufang Li,Yangliu Kuai,Yali Li,Yi Luo,Yifan Zhang,Yiling Liu,Ying Li,Yong Wang,Yongtao Wang,Yuanwei Wu,Yue Fan,Yunchao Wei,Yuqin Zhang,Zexin Wang,Zhangyang Wang,Zhaoyue Xia,Zhen Cui,Zhenwei He,Zhipeng Deng,Zhiyao Guo,Zichen Song +104 more
TL;DR: A large-scale drone-based dataset, including 8, 599 images with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc, is released, to narrow the gap between current object detection performance and the real-world requirements.
Proceedings ArticleDOI
VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results
Dawei Du,Yue Zhang,Zexin Wang,Zhikang Wang,Zichen Song,Ziming Liu,Liefeng Bo,Hailin Shi,Rui Zhu,Aashish Kumar,Aijin Li,Almaz Zinollayev,Anuar Askergaliyev,Arne Schumann,Binjie Mao,Pengfei Zhu,Byeongwon Lee,Chang Liu,Changrui Chen,Chunhong Pan,Chunlei Huo,Da Yu,DeChun Cong,Dening Zeng,Dheeraj Reddy Pailla,Di Li,Longyin Wen,Dong Wang,Donghyeon Cho,Dongyu Zhang,Furui Bai,George Jose,Guangyu Gao,Guizhong Liu,Haitao Xiong,Hao Qi,Haoran Wang,Xiao Bian,Heqian Qiu,Hongliang Li,Huchuan Lu,Ildoo Kim,Jaekyum Kim,Jane Shen,Jihoon Lee,Jing Ge,Jingjing Xu,Jingkai Zhou,Haibin Lin,Jonas Meier,Jun Won Choi,Junhao Hu,Junyi Zhang,Junying Huang,Kaiqi Huang,Keyang Wang,Lars Sommer,Lei Jin,Lei Zhang,Qinghua Hu,Lianghua Huang,Lin Sun,Lucas Steinmann,Meixia Jia,Nuo Xu,Pengyi Zhang,Qiang Chen,Qingxuan Lv,Qiong Liu,Qishang Cheng,Tao Peng,Sai Saketh Chennamsetty,Shuhao Chen,Shuo Wei,Srinivas S S Kruthiventi,Sungeun Hong,Sungil Kang,Tong Wu,Tuo Feng,Varghese Alex Kollerathu,Wanqi Li,Jiayu Zheng,Wei Dai,Weida Qin,Weiyang Wang,Xiaorui Wang,Xiaoyu Chen,Xin Chen,Xin Sun,Xin Zhang,Xin Zhao,Xindi Zhang,Xinyao Wang,Xinyu Zhang,Xuankun Chen,Xudong Wei,Xuzhang Zhang,Yanchao Li,Yifu Chen,Yu Heng Toh,Yu Zhang,Yu Zhu,Yunxin Zhong +102 more
TL;DR: The Vision Meets Drone Object Detection in Image Challenge (VME-DET 2019) as discussed by the authors, held in conjunction with the 17th International Conference on Computer Vision (ICCV 2019), focuses on image object detection on drones.
Journal ArticleDOI
Parametric Deformable Exponential Linear Units for deep neural networks.
TL;DR: A Parametric Deformable Exponential Linear Unit (PDELU) is proposed and theoretically verify its effectiveness for improving the convergence speed of learning procedure and by means of flexible map shape, it could push the mean value of activation responses closer to zero, which ensures the steepest descent in training a deep neural network.
Book ChapterDOI
Learning with Noisy Class Labels for Instance Segmentation.
TL;DR: A novel method is proposed in this paper, which uses different losses describing different roles of noisy class labels to enhance the learning in instance segmentation.
Journal ArticleDOI
Hybrid-loss supervision for deep neural network
TL;DR: A Hybrid-loss supervision (HLS) framework is proposed in order to obtain smoother and more spatially consistent features with shared FC layers and can significantly boost the efficiency of existing convolution networks for both image classification task and object detection task without increasing network parameters and computational complexity.