no code implementations • 10 Mar 2021 • Chunbin Gu, Jiajun Bu, Xixi Zhou, Chengwei Yao, Dongfang Ma, Zhi Yu, Xifeng Yan
Prior work usually uses a three-stage strategy to tackle this task: 1) extract the features of the inputs; 2) fuse the feature of the source image and its modified text to obtain fusion feature; 3) learn a similarity metric between the desired image and the source image + modified text by using deep metric learning.
no code implementations • 18 Mar 2020 • Ning Ma, Jiajun Bu, Jieyu Yang, Zhen Zhang, Chengwei Yao, Zhi Yu, Sheng Zhou, Xifeng Yan
The shared sub-structures between training classes and test classes are essential in few-shot graph classification.
no code implementations • 9 Mar 2020 • Xixi Zhou, Chengxi Li, Jiajun Bu, Chengwei Yao, Keyue Shi, Zhi Yu, Zhou Yu
Our approach, Text matching with Deep Info Max (TIM), is integrated with a procedure of unsupervised learning of representations by maximizing the mutual information between text matching neural network's input and output.
3 code implementations • 14 Nov 2019 • Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Chengwei Yao, Zhi Yu, Can Wang
HGP-SL incorporates graph pooling and structure learning into a unified module to generate hierarchical representations of graphs.
Ranked #1 on Graph Classification on PROTEINS