7 citations · 24 across the 16 of their papers we have counts for
7 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…
EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning
Shiming Chen, Shihuang Chen, Wenjin Hou +2
Zero-shot learning (ZSL) aims to recognize the novel classes which cannot be collected for training a prediction model. Accordingly, generative models (e.g., generative adversarial…
Evolving Semantic Prototype Improves Generative Zero-Shot Learning
Shiming Chen, Wenjin Hou, Ziming Hong +5
In zero-shot learning (ZSL), generative methods synthesize class-related sample features based on predefined semantic prototypes. They advance the ZSL performance by synthesizing u…
Coping with Change: Learning Invariant and Minimum Sufficient Representations for Fine-Grained Visual Categorization
Shuo Ye, Shujian Yu, Wenjin Hou +2
Fine-grained visual categorization (FGVC) is a challenging task due to similar visual appearances between various species. Previous studies always implicitly assume that the traini…
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…
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.…