Xiang Li (李翔)
Department of Computer Science and Technology, Nanjing University of Science and Technology (NJUST)
Address: 200 Avenue, Xuanwu District, Nanjing, China
Email: xiang.li.implus [at] {njust.edu.cn}

About Me [GitHub] [Google Scholar]

I got my PhD degree from the Department of Computer Science and Technology, Nanjing University of Science and Technology (NJUST) in 2020. I started my postdoctoral career in NJUST as a candidate for the 2020 Postdoctoral Innovative Talent Program. My advisor is Prof. Jian Yang from NJUST, who is a Changjiang Scholar. My vice-advisor is Prof. Xiaolin Hu from Tsinghua University. In 2016, I spent 8 months as a research intern in Microsoft Research Asia, supervised by Prof. Tao Qin and Prof. Tie-Yan Liu. I am also a long-term visiting scholar at Momenta, mainly focusing on monocular perception algorithm.

My recent works are mainly on monocular perception, object detection/recognition, unsupervised learning, big data and neural architecture design.

Honor

  • Champion of 2015 Alibaba Tianchi's first big data competition, namely Ali mobile recommendation algorithm, 300,000 RMB bonus (1st from 7186 team)
  • Champion of 2016 Didi Tech Di-Tech's first big data competition, namely the travel demand prediction algorithm, 100,000 US dollars bonus (1st from 7664 team)
  • Second place of 2020 Zhengtu Cup's first AI competition, namely the industrial defect detection algorithm, 150,000 RMB bonus (2st from 900 teams)
  • 2015 Dean Medal of School of Computer Science, Nanjing University of Science and Technology, 2016 Presidential Medal of Nanjing University of Science and Technology, 2016 National Scholarship
  • ACM-ICPC Asia Regional Contest, Silver Medal (1st)

News

  • 1 paper accepted in CVPR 2021.
  • 1 paper accepted in NeurIPS 2020.
  • 1 paper accepted in AAAI 2020.
  • 3 papers accepted in CVPR 2019.
  • 1 paper accepted in CVPR 2018.
  • 1 paper accepted in IJCAI 2018.
  • 1 paper accepted in NeurIPS 2016.

Selected Publications

Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection
Xiang Li, Wenhai Wang, Xiaolin Hu, Jun Li, Jinhui Tang, Jian Yang
in CVPR, 2021
[Paper] [Code] [BibTex]
The improved version of GFocal!
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
Xiang Li*, Wenhai Wang, Lijun Wu, Shuo Chen, Xiaolin Hu, Jun Li, Jinhui Tang, Jian Yang
in NeurIPS, 2020
[Paper] [Code]
We propose the generalized focal loss for learning the improved representations of dense object detector. GFocal is officially included in [MMDetection], and is an important part of the [winning solution] in GigaVision contest (object detection and tracking tracks) hosted in ECCV 2020 workshop (winner: DeepBlueAI team).
Selective kernel networks
Xiang Li*, Wenhai Wang, Xiaolin Hu, Jian Yang
in CVPR, 2019
[Paper] [BibTex] [Code]
We propose a selective kernel mechanism for convolution.
Understanding the disharmony between dropout and batch normalization by variance shift
Xiang Li*, Shuo Chen, Xiaolin Hu, Jian Yang
in CVPR, 2019
[Paper] [BibTex]
We explore and address the disharmony between dropout and batch normalization.
Understanding the disharmony between weight normalization family and weight decay
Xiang Li*, Shuo Chen, Jian Yang
in AAAI, 2020
[Paper]
We explore and address the disharmony between weight normalization family and weight decay.
LightRNN: Memory and computation-efficient recurrent neural networks
Xiang Li*, Tao Qin, Jian Yang, Tie-Yan Liu
in NeurIPS, 2016
[Paper] [BibTex]
We propose a memory and computation-efficient recurrent neural networks for language model.
Stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal
Jifeng Wang*, Xiang Li*, Jian Yang
in CVPR, 2018
[Paper] [BibTex] [Dataset]
We release a new dataset for jointly shadow detection and removal.
Shape Robust Text Detection with Progressive Scale Expansion Network
Wenhai Wang*, Enze Xie*, Xiang Li*, Wenbo Hou, Tong Lu, Gang Yu, Shuai Shao
in CVPR, 2019
[Paper] [Poster] [BibTex] [Code]
We proposed a segmentation-based text detector that can precisely detect text instances with arbitrary shapes.
Mixed Link Networks
Wenhai Wang*, Xiang Li*, Jian Yang, Tong Lu
in IJCAI, 2018
[Paper] [Poster] [BibTex] [Code]
We proposed an parameter-efficient convolutional neural networks for image classification.

Review Services

Journal Reviewer
TNNLS
Conference Reviewer
CVPR: 2020, 2021
AAAI: 2019, 2020, 2021