Qiankun Ding — accepted AI-conference papers

2023

Uni-Mol: A Universal 3D Molecular Representation Learning Framework

Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei +2

ICLR 2023poster

Molecular representation learning (MRL) has gained tremendous attention due to its critical role in learning from limited supervised data for applications like drug design. In most MRL methods, molecules are treated as 1D sequential tokens or 2D topology graphs, limiting their ability to incorporate…

Cited by 377 Source ↗PDF ↗code ↗Scholar ↗

2022

Data Association between Event Streams and Intensity Frames under Diverse Baselines

Dehao Zhang, Qiankun Ding, Peiqi Duan, Chu Zhou, Boxin Shi

ECCV 2022poster

"This paper proposes a learning-based framework to associate event streams and intensity frames under diverse camera baselines, to simultaneously benefit to camera pose estimation under large baseline and depth estimation under small baseline. Based on the observation that event streams are globally…

Cited by 11 Source ↗PDF ↗Scholar ↗