32 citations · 89 across the 11 of their papers we have counts for
11 papers
SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs
Sheng Tian, Jihai Dong, Jintang Li +7
Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones. As instances that appear in the real world are naturally conne…
Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs
Jintang Li, Sheng Tian, Ruofan Wu +6
The prevalence of large-scale graphs poses great challenges in time and storage for training and deploying graph neural networks (GNNs). Several recent works have explored solution…
AxWin Transformer: A Context-Aware Vision Transformer Backbone with Axial Windows
Fangjian Lin, Yizhe Ma, Sitong Wu +2
Recently Transformer has shown good performance in several vision tasks due to its powerful modeling capabilities. To reduce the quadratic complexity caused by the attention, some…
Exploring vision transformer layer choosing for semantic segmentation
Fangjian Lin, Yizhe Ma, Shengwei Tian
Extensive work has demonstrated the effectiveness of Vision Transformers. The plain Vision Transformer tends to obtain multi-scale features by selecting fixed layers, or the last l…
PRSeg: A Lightweight Patch Rotate MLP Decoder for Semantic Segmentation
Yizhe Ma, Fangjian Lin, Sitong Wu +2
The lightweight MLP-based decoder has become increasingly promising for semantic segmentation. However, the channel-wise MLP cannot expand the receptive fields, lacking the context…
HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification
Xiangzuo Huo, Gang Sun, Shengwei Tian +5
Medical image classification has developed rapidly under the impetus of the convolutional neural network (CNN). Due to the fixed size of the receptive field of the convolution kern…
Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks
Jintang Li, Zhouxin Yu, Zulun Zhu +6
Recent years have seen a surge in research on dynamic graph representation learning, which aims to model temporal graphs that are dynamic and evolving constantly over time. However…
What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders
Jintang Li, Ruofan Wu, Wangbin Sun +6
The last years have witnessed the emergence of a promising self-supervised learning strategy, referred to as masked autoencoding. However, there is a lack of theoretical understand…
Feature Selective Transformer for Semantic Image Segmentation
Fangjian Lin, Tianyi Wu, Sitong Wu +2
Recently, it has attracted more and more attentions to fuse multi-scale features for semantic image segmentation. Various works were proposed to employ progressive local or global…
StructToken : Rethinking Semantic Segmentation with Structural Prior
Fangjian Lin, Zhanhao Liang, Sitong Wu +3
In previous deep-learning-based methods, semantic segmentation has been regarded as a static or dynamic per-pixel classification task, \textit{i.e.,} classify each pixel representa…
Fully Transformer Networks for Semantic Image Segmentation
Sitong Wu, Tianyi Wu, Fangjian Lin +2
Transformers have shown impressive performance in various natural language processing and computer vision tasks, due to the capability of modeling long-range dependencies. Recent p…