84 citations · 208 across the 30 of their papers we have counts for
6 papers · 1 filter
Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing
Sichao Fu, Qinmu Peng, Yang He +2
In recent years, graph neural networks (GNN) have achieved significant developments in a variety of graph analytical tasks. Nevertheless, GNN's superior performance will suffer fro…
Detail Reinforcement Diffusion Model: Augmentation Fine-Grained Visual Categorization in Few-Shot Conditions
Tianxu Wu, Shuo Ye, Shuhuang Chen +2
The challenge in fine-grained visual categorization lies in how to explore the subtle differences between different subclasses and achieve accurate discrimination. Previous researc…
Another Vertical View: A Hierarchical Network for Heterogeneous Trajectory Prediction via Spectrums
Beihao Xia, Conghao Wong, Duanquan Xu +2
With the fast development of AI-related techniques, the applications of trajectory prediction are no longer limited to easier scenes and trajectories. More and more trajectories wi…
Semantic-visual Guided Transformer for Few-shot Class-incremental Learning
Wenhao Qiu, Sichao Fu, Jingyi Zhang +2
Few-shot class-incremental learning (FSCIL) has recently attracted extensive attention in various areas. Existing FSCIL methods highly depend on the robustness of the feature backb…
Filter Pruning based on Information Capacity and Independence
Xiaolong Tang, Shuo Ye, Yufeng Shi +3
Filter pruning has gained widespread adoption for the purpose of compressing and speeding up convolutional neural networks (CNNs). However, existing approaches are still far from p…
Self-supervised Guided Hypergraph Feature Propagation for Semi-supervised Classification with Missing Node Features
Chengxiang Lei, Sichao Fu, Yuetian Wang +4
Graph neural networks (GNNs) with missing node features have recently received increasing interest. Such missing node features seriously hurt the performance of the existing GNNs.…