I am a fourth-year CS Ph.D student at the University of Rochester, supervised by Chenliang Xu.
I got my bechalor degree from the University of Electronic Sience and Techonology of China.
My interest spans deep learning, machine learning, and computer vision.

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Wegmans 2403


[Jul/2021] Three papers accepted by ICCV2021: two about language and scene understanding, one about language driven image editing.
[Mar/2021] Our language driven image editing paper is accepted by CVPR2021.
[Sep/2020] Our language driven image editing paper is accepted by ACCV2020 Oral.
[Feb/2019] Our weakly supervised video grounding paper is accepted by CVPR 2019.


A Simple Baseline for Weakly-Supervised Scene Graph Generation
Jing Shi, Yiwu Zhong, Ning Xu, Yin Li, Chenliang Xu
International Conference on Computer Vision (ICCV), 2021
Paper comming soon

We construct a versatile baseline for Weakly-Supervised Scene Graph Generation.

Learning to Generate Scene Graph from Natural Language Supervision
Yiwu Zhong, Jing Shi, Jianwei Yang, Chenliang Xu Yin Li,
International Conference on Computer Vision (ICCV), 2021
Paper comming soon

We generate scene graph from only natural language supervision.

Language-Guided Global Image Editing via Cross-Modal Cyclic Mechanism
Wentao Jiang, Ning Xu, Jiayun Wang, Chen Gao, Jing Shi, Zhe Lin, Si Liu ,
International Conference on Computer Vision (ICCV), 2021
Paper comming soon

We tackle language-guided global image editing via GAN model with cross-modal consistency

Learning by Planning: Language-Guided Global Image Editing
Jing Shi, Ning Xu, Trung Bui, Franck Dernoncourt, Chenliang Xu
Computer Vision and Pattern Recognition (CVPR), 2021
project page / code&dataset

We contruct a new framework for language-guided global image editing.

Cubic Spline Smoothing Compensation for Irregularly Sampled Sequences
Jing Shi, Jing Bi, Yingru Liu, Chenliang Xu
Arxiv Preprint

We introduce the cubic spline smoothing compensation upon ODE-RNN model for irregularly observed time series.

Learning Continuous-Time Dynamics by Stochastic Differential Networks
Yingru Liu, Yucheng Xing, Xuewen Yang, Xin Wang, Jing Shi, Di Jin, Zhaoyue Chen
Arxiv Preprint

A Benchmark and Baseline for Language-Driven Image Editing
Jing Shi, Ning Xu, Trung Bui, Franck Dernoncourt, Zheng Wen, Chenliang Xu
Asian Conference on Computer Vision (ACCV), 2020 (Oral)
code / dataset

We introduce the GIER dataset and a pipeline for language-driven image editing.

GAN-EM: GAN Based EM Learning Framework
Wentian Zhao*, Shaojie Wang*, Zhihuai Xie, Jing Shi, Chenliang Xu
International Joint Conference on Artificial Intelligence (IJCAI), 2019

Not All Frames Are Equal: Weakly-Supervised Video Groundingwith Contextual Similarity and Visual Clustering Losses
Jing Shi, Jia Xu, Boqing Gong, Chenliang Xu
Computer Vision and Pattern Recognition (CVPR), 2019
project page/ code

We introduce contextual smilarity loss and visual clustering loss for weakly supervised video grounding.

Audio-Visual Event Localization in Unconstrained Videos
Yapeng Tian, Jing Shi, Bochen Li, Zhiyao Duan, Chenliang Xu
European Conference on Computer Vision (ECCV), 2018
project page/ code

We introduce audio-visual event localization to understand the interaction of vision and audio.

Boundary Vibration Control of Variable Length Crane Systems in Two Dimensional Space with Output Constraints
Xiuyu He, Wei He, Jing Shi, Changyin Sun
IEEE/ASME Transactions on Mechatronics (TMech), 2017

We demonstrate the lyapunov stability of the boundary control for the moving crane system in 2-D space.

Impact Damage Detection and Characterization using Eddy Current Pulsed Thermography
Yizhe Wang, Han Ke, Jing Shi, Bing Gao, Guiyun Tian
IEEE Far East NDT New Technology & Application Forum (FENDT), 2016

We use eddy current pulse thermography to detect impact damages of torque arm in aircraft brake system.


Differential Network for Video Object Detection
Jing Shi, Chenliang Xu
CSC 577 Advanced Topics in Computer Vision, 2017

For flow based video obejct detection, we propose the differential network to select the key frame. Then we adapt the sequential NMS to an incremental form so that it is both time-efficient and accurate.

Tencent AI Lab ,    Shenzhen,    May - Aug 2018

Collaborator: Jia Xu, Boqing Gong
Project: Weakly supervised video grounding.

Adobe Research ,    San Jose,    May - Sep 2019

Collaborator: Ning Xu,
Project: Language Driven Image Editing.


CSC 400 Problem Seminar CSC 440 Data Mining
CSC 454 Program Language Design and Implementation CSC 577 Advanced Topics in Computer Vision
CSC 446 Machine Learning CSC 484 Advanced Algorithms
CSC 455 Software Analysis and Improvement CSC 453 Dynamic Language and Software Development
CSC 480 Computing Model and Limitation CSC 249/449 Machine Vision [Teaching Assistant]


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