Shuangrui Ding (丁双睿)

I am a master student at Electrical Engineering Department of Shanghai Jiao Tong University, advised by Prof. Hongkai Xiong. Prior to that, I obtained my bachelor degree of Computer Science at University of Michigan, with a dual degree of Electrical and Computer Engineering at Shanghai Jiao Tong University in 2021.

During my undergraduate, I have been a member in Foreseer at Umich, where I was advised by Prof. Qiaozhu Mei and mentored by Jiaqi Ma. I also interned at Tencent AI Lab, lead by Jue Wang.

I'm interested in computer vision and deep learning. Currently, I am working closely with Prof. Weidi Xie on video representation learning.

Please drop me an email if you are interested in corporation with we.

Email  /  CV  /  Google Scholar /  Github

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News

[Jul. 2022] Our paper Static and Dynamic Concepts for Self-supervised Video Representation Learning was accepted at ECCV 2022.

[Jun. 2022] Our paper Dual Contrastive Learning for Spatio-temporal Representation was accepted at ACM MM 2022.

[Mar. 2022] Our paper Motion-aware Contrastive Video Representation Learning via Foreground-background Merging was accepted at CVPR 2022.

Preprint
Motion-inductive Self-supervised Object Discovery in Videos
Shuangrui Ding, Weidi Xie, Yabo Chen, Rui Qian, Xiaopeng Zhang, Hongkai Xiong, Qi Tian
preprint, 2022
arXiv

We propose a motion-inductive model through directly processing consecutive RGB frames to segment the foreground objects and train it without any mask annotations.

Publications(* equal contribution)
Static and Dynamic Concepts for Self-supervised Video Representation Learning
Rui Qian, Shuangrui Ding, Xian Liu, Dahua Lin
ECCV, 2022
arXiv / code / slide

Learn static and dynamic visual concepts in videos to aggregate local patterns with similar semantics to boost unsupervised video representation.

Dual Contrastive Learning for Spatio-temporal Representation
Shuangrui Ding, Rui Qian, Hongkai Xiong
ACM MM, 2022
arXiv / poster / video

A novel dual contrastive formulation is presented to decouple the static/dynamic features and thus mitigate the background bias.

Motion-aware Contrastive Video Representation Learning via Foreground-background Merging
Shuangrui Ding, Maomao Li, Tianyu Yang, Rui Qian, Haohang Xu, Qingyi Chen, Jue Wang, Hongkai Xiong
CVPR, 2022
Project page / arXiv / code / Chinese coverage / poster

Mitigate the background bias in self-supervised video representation learning via copy-pasting the foreground onto the other backgrounds.

Enhancing Self-supervised Video Representation Learning via Multi-level Feature Optimization
Rui Qian, Yuxi Li, Huabin Liu, John See, Shuangrui Ding, Xian Liu, Dian Li, Weiyao Lin
ICCV, 2021
arXiv / code

Self-supervised video representation learning from the perspective of both high-level semantics and lower-level characteristics

Towards More Practical Adversarial Attacks on Graph Neural Networks
Jiaqi Ma*, Shuangrui Ding*, Qiaozhu Mei
NeurIPS, 2020
arXiv / slides / video / code

Exploiting the structural inductive biases of GNNs, the restricted black-box adversarial attacks can be conducted effectively.

Awards

Graduate National Scholarship (Top 2%), Ministry of Education of China. 2022

Shanghai Excellent Graduate (Top 5%), Shanghai Municipal Education Commission. 2021

Finalist winner (Top 0.3%), Mathematical Contest in Modeling. 2019

National Scholarship (Top 2%), Ministry of Education of China. 2018

Professional Services

  • Reviewer: ECCV'22, AAAI'23, ICLR'23.
  • Misc

    1. My favorite sports is soccer. I was the captain of UM-SJTU JI soccer team during season 2018. Besides, I am a super fan of Manchester City in Premier League. The Viking is smashing the league🔥

    2. I am proud that I have graudated from the competition class at Hangzhou No.2 High school, where I make friends with so many talented students and prestigious teachers.

    3. It is worth mentioning that Rui is my best friend and has motivated me forward for over ten years as my role model. Best wishes and good luck!



    Updated at Oct. 2022
    Thanks Jon Barron for this amazing template.