no code implementations • Findings (EMNLP) 2021 • Jinfa Yang, Yongjie Shi, Xin Tong, Robin Wang, Taiyan Chen, Xianghua Ying
By using previous knowledge graph embedding methods, every entity in a knowledge graph is usually represented as a k-dimensional vector.
no code implementations • Findings (ACL) 2022 • Jinfa Yang, Xianghua Ying, Yongjie Shi, Xin Tong, Ruibin Wang, Taiyan Chen, Bowei Xing
The recently proposed Limit-based Scoring Loss independently limits the range of positive and negative triplet scores.
1 code implementation • COLING 2022 • Jinfa Yang, Xianghua Ying, Yongjie Shi, Xin Tong, Ruibin Wang, Taiyan Chen, Bowei Xing
It is crucial for knowledge graph embedding models to model and infer various relation patterns, such as symmetry/antisymmetry.
no code implementations • CVPR 2022 • Xin Tong, Xianghua Ying, Yongjie Shi, Ruibin Wang, Jinfa Yang
To achieve this goal, we propose a novel Transformer based Line segment Classifier (TLC) that can group line segments in images and estimate the corresponding vanishing points.
no code implementations • ECCV 2020 • Yunhao Ba, Alex Ross Gilbert, Franklin Wang, Jinfa Yang, Rui Chen, Yiqin Wang, Lei Yan, Boxin Shi, Achuta Kadambi
This paper makes a first attempt to bring the Shape from Polarization (SfP) problem to the realm of deep learning.