J
Jingkai Zhou
Researcher at South China University of Technology
Publications - 16
Citations - 428
Jingkai Zhou is an academic researcher from South China University of Technology. The author has contributed to research in topics: Object detection & Drone. The author has an hindex of 5, co-authored 13 publications receiving 160 citations. Previous affiliations of Jingkai Zhou include Alibaba Group.
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
Decoupled Dynamic Filter Networks
Abstract: Convolution is one of the basic building blocks of CNN architectures. Despite its common use, standard convolution has two main shortcomings: Content-agnostic and Computation-heavy. Dynamic filters are content-adaptive, while further increasing the computational overhead. Depth-wise convolution is a lightweight variant, but it usually leads to a drop in CNN performance or requires a larger number of channels. In this work, we propose the Decoupled Dynamic Filter (DDF) that can simultaneously tackle both of these shortcomings. Inspired by recent advances in attention, DDF decouples a depth-wise dynamic filter into spatial and channel dynamic filters. This decomposition considerably reduces the number of parameters and limits computational costs to the same level as depth-wise convolution. Meanwhile, we observe a significant boost in performance when replacing standard convolution with DDF in classification networks. ResNet50 / 101 get improved by 1.9% and 1.3% on the top-1 accuracy, while their computational costs are reduced by nearly half. Experiments on the detection and joint upsampling networks also demonstrate the superior performance of the DDF upsampling variant (DDF-Up) in comparison with standard convolution and specialized content-adaptive layers. The project page with code is available 1.
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.
Proceedings ArticleDOI
VisDrone-VID2019: The Vision Meets Drone Object Detection in Video Challenge Results
Pengfei Zhu,Yue Zhang,Liefeng Bo,Hailin Shi,Rui Zhu,Bing Dong,Dheeraj Reddy Pailla,Feng Ni,Guangyu Gao,Guizhong Liu,Haitao Xiong,Dawei Du,Jing Ge,Jingkai Zhou,Jinrong Hu,Lin Sun,Long Chen,Martin Lauer,Qiong Liu,Sai Saketh Chennamsetty,Ting Sun,Tong Wu,Longyin Wen,Varghese Alex Kollerathu,Wei Tian,Weida Qin,Xier Chen,Xingjie Zhao,Yanchao Lian,Yinan Wu,Ying Li,Yingping Li,Yiwen Wang,Xiao Bian,Yuduo Song,Yuehan Yao,Yunfeng Zhang,Zhaoliang Pi,Zhaotang Chen,Zhenyu Xu,Zhibin Xiao,Zhipeng Luo,Ziming Liu,Haibin Ling,Qinghua Hu,Tao Peng,Jiayu Zheng,Xinyao Wang +47 more
TL;DR: The goal is to advance the state-of-the-art detection algorithms and provide a comprehensive evaluation platform for them and demonstrate that there still remains a large room for improvement for object detection algorithms on drones.
Journal ArticleDOI
Benchmarking a large-scale FIR dataset for on-road pedestrian detection
TL;DR: A nighttime FIR pedestrian dataset with the largest scale at present is introduced in this paper, which is called SCUT (South China University of Technology) dataset and shows that convolutional neural networks (CNN) based detectors obtained good performance on FIR image.