1 code implementation • 21 Sep 2023 • Meng Liu, Ke Liang, Dayu Hu, Hao Yu, Yue Liu, Lingyuan Meng, Wenxuan Tu, Sihang Zhou, Xinwang Liu
We observe that these audiovisual data naturally have temporal attributes, such as the time information for each frame in the video.
no code implementations • 6 Jul 2023 • Ke Liang, Sihang Zhou, Yue Liu, Lingyuan Meng, Meng Liu, Xinwang Liu
To this end, we propose the graph Structure Guided Multimodal Pretrained Transformer for knowledge graph reasoning, termed SGMPT.
no code implementations • 23 May 2023 • Ke Liang, Lingyuan Meng, Sihang Zhou, Siwei Wang, Wenxuan Tu, Yue Liu, Meng Liu, Xinwang Liu
However, the uni-directional message-passing mechanism hinders such models from exploiting hidden mutual relations between entities in directed graphs.
no code implementations • 20 Apr 2023 • Lingyuan Meng, Ke Liang, Bin Xiao, Sihang Zhou, Yue Liu, Meng Liu, Xihong Yang, Xinwang Liu
Moreover, most of the existing methods ignore leveraging the beneficial information from aliasing relations (AR), i. e., data-rich relations with similar contextual semantics to the target data-poor relation.
1 code implementation • 12 Dec 2022 • Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu, Fuchun Sun
According to the graph types, existing KGR models can be roughly divided into three categories, i. e., static models, temporal models, and multi-modal models.