108 citations · 140 across the 10 of their papers we have counts for
11 papers
Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method
Shuo Han, Jiacheng Liu, Jiayun Wu +2
Graph Representation Learning (GRL) is an influential methodology, enabling a more profound understanding of graph-structured data and aiding graph clustering, a critical task acro…
A Theory of General Difference in Continuous and Discrete Domain
Linmi Tao, Ruiyang Liu, Donglai Tao +4
Though a core element of the digital age, numerical difference algorithms struggle with noise susceptibility. This stems from a key disconnect between the infinitesimal quantities…
Significant Ties Graph Neural Networks for Continuous-Time Temporal Networks Modeling
Jiayun Wu, Tao Jia, Yansong Wang +1
Temporal networks are suitable for modeling complex evolving systems. It has a wide range of applications, such as social network analysis, recommender systems, and epidemiology. R…
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
Ningyu Zhang, Xin Xu, Liankuan Tao +19
We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base pop…
Are we ready for a new paradigm shift? A Survey on Visual Deep MLP
Ruiyang Liu, Yinghui Li, Linmi Tao +2
Recently, the proposed deep MLP models have stirred up a lot of interest in the vision community. Historically, the availability of larger datasets combined with increased computin…
Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation
Mst. Tasnim Pervin, Linmi Tao, Aminul Huq +2
Segmentation is considered to be a very crucial task in medical image analysis. This task has been easier since deep learning models have taken over with its high performing behavi…
Towards All-around Knowledge Transferring: Learning From Task-irrelevant Labels
Yinghui Li, Ruiyang Liu, ZiHao Zhang +4
Deep neural models have hitherto achieved significant performances on numerous classification tasks, but meanwhile require sufficient manually annotated data. Since it is extremely…
Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework
Li Tao, Xueting Wang, Toshihiko Yamasaki
We propose a self-supervised method to learn feature representations from videos. A standard approach in traditional self-supervised methods uses positive-negative data pairs to tr…
Motion Representation Using Residual Frames with 3D CNN
Li Tao, Xueting Wang, Toshihiko Yamasaki
Recently, 3D convolutional networks (3D ConvNets) yield good performance in action recognition. However, optical flow stream is still needed to ensure better performance, the cost…
Weakly Supervised Video Summarization by Hierarchical Reinforcement Learning
Yiyan Chen, Li Tao, Xueting Wang +1
Conventional video summarization approaches based on reinforcement learning have the problem that the reward can only be received after the whole summary is generated. Such kind of…
Rethinking Motion Representation: Residual Frames with 3D ConvNets for Better Action Recognition
Li Tao, Xueting Wang, Toshihiko Yamasaki
Recently, 3D convolutional networks yield good performance in action recognition. However, optical flow stream is still needed to ensure better performance, the cost of which is ve…