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 • 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 • CVPR 2022 • Takashi Isobe, Xu Jia, Xin Tao, Changlin Li, Ruihuang Li, Yongjie Shi, Jing Mu, Huchuan Lu, Yu-Wing Tai
Instead of directly feeding consecutive frames into a VSR model, we propose to compute the temporal difference between frames and divide those pixels into two subsets according to the level of difference.
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 • CVPR 2021 • Takashi Isobe, Xu Jia, Shuaijun Chen, Jianzhong He, Yongjie Shi, Jianzhuang Liu, Huchuan Lu, Shengjin Wang
To obtain a single model that works across multiple target domains, we propose to simultaneously learn a student model which is trained to not only imitate the output of each expert on the corresponding target domain, but also to pull different expert close to each other with regularization on their weights.
Ranked #4 on Domain Adaptation on GTAV to Cityscapes+Mapillary
1 code implementation • CVPR 2021 • Shuaijun Chen, Xu Jia, Jianzhong He, Yongjie Shi, Jianzhuang Liu
To address the task of SSDA, a novel framework based on dual-level domain mixing is proposed.