Yiran Zhong is a principal investigator at Shanghai AI Laboratory. Prior to that, he received a Ph.D. degree in Engineering from The Australian National University, Canberra, Australia in 2021 and an M.Eng with the first class honor in information and electronics engineering from The Australian National University, Canberra, Australia, in 2014, and a B.E. degree from the University of Electronic Science and Technology of China in 2008. His research interests include self-supervised learning, visual geometry learning, multimodality learning, machine learning, and natural language processing. He won the ICIP Best Student Paper Award in 2014.
Education
Doctor of Philosophy (2016 – 2020)
Supervisor: Prof. Hongdong Li, Prof. Yuchao Dai, Reader Henry Gardner, Prof. Nicholas Barnes, Prof. Richard Hartley @ ANU
Thesis: Self-supervised Visual Geometry Learning
Masters of Engineering (First Class Honor) (2012 – 2014)
Supervisor: Prof. Hongdong Li @ ANU
Thesis: Interactive 3D Reconstruction of Insects
Bachelor of Automation (2008 – 2012)
@ UESTC
Thesis: Multi-media Sand Table System Design
Publication
2022
Displacement-Invariant Cost Computation for Stereo Matching, IJCV, 2022
Implicit Motion Handling for Video Camouflaged Object Detection, CVPR, 2022
Deep Laparoscopic Stereo Matching with Transformers, MICCAI, 2022
Audio-Visual Segmentation, ECCV, 2022
cosFormer: Rethinking Softmax In Attention, ICLR, 2022
Transcribing Natural Languages for The Deaf via Neural Editing Programs, AAAI, 2022
2021
Deep robust image deblurring via blur distilling and information comparison in latent space, Neurocomputing, 2021
RGB-D Saliency Detection via Cascaded Mutual Information Minimization, ICCV, 2021
Positive Sample Propagation along the Audio-Visual Event Line, CVPR, 2021
Deep Two-View Structure-from-Motion Revisited, CVPR, 2021
ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring, CVPR, 2021
2020
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation, NeurIPS, 2020
Hierarchical Neural Architecture Search for Deep Stereo Matching, NeurIPS, 2020
Deblurring by realistic blurring, CVPR, 2020
2019
Unsupervised deep epipolar flow for stationary or dynamic scenes, CVPR, 2019
Noise-aware unsupervised deep lidar-stereo fusion, CVPR, 2019
2018
Adversarial spatio-temporal learning for video deblurring, TIP, 2018
3D geometry-aware semantic labeling of outdoor street scenes, ICPR, 2018
Open-world stereo video matching with deep rnn, ECCV, 2018
Stereo computation for a single mixture image, ECCV, 2018
2016
Robust multi-body feature tracker: a segmentation-free approach, CVPR, 2016
Null space clustering with applications to motion segmentation and face clustering, ICIP, 2014
YIRAN.怡然 