18 citations · 47 across the 11 of their papers we have counts for
11 papers
Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation
Runlong Cao, Ying Zang, Chuanwei Zhou +4
Semi-supervised referring expression segmentation (SS-RES) aims to achieve precise pixel-level language grounding under limited annotation, yet suffers from limited supervision and…
UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge Graphs
Yide Qiu, Shaoxiang Ling, Tong Zhang +2
Irregular data in real-world are usually organized as heterogeneous graphs (HGs) consisting of multiple types of nodes and edges. To explore useful knowledge from real-world data,…
Structure-Sensitive Graph Dictionary Embedding for Graph Classification
Guangbu Liu, Tong Zhang, Xudong Wang +3
Graph structure expression plays a vital role in distinguishing various graphs. In this work, we propose a Structure-Sensitive Graph Dictionary Embedding (SS-GDE) framework to tran…
Going Deeper in Frequency Convolutional Neural Network: A Theoretical Perspective
Xiaohan Zhu, Zhen Cui, Tong Zhang +2
Convolutional neural network (CNN) is one of the most widely-used successful architectures in the era of deep learning. However, the high-computational cost of CNN still hampers mo…
Spatial-Temporal Tensor Graph Convolutional Network for Traffic Prediction
Xuran Xu, Tong Zhang, Chunyan Xu +2
Accurate traffic prediction is crucial to the guidance and management of urban traffics. However, most of the existing traffic prediction models do not consider the computational b…
Interest-Behaviour Multiplicative Network for Resource-limited Recommendation
Qianliang Wu, Tong Zhang, Zhen Cui +1
Resource constraints, e.g. limited product inventory or financial strength, may affect consumers' choices or preferences in some recommendation tasks but are usually ignored in pre…
Instance-Aware Graph Convolutional Network for Multi-Label Classification
Yun Wang, Tong Zhang, Zhen Cui +2
Graph convolutional neural network (GCN) has effectively boosted the multi-label image recognition task by introducing label dependencies based on statistical label co-occurrence o…
Graph Wasserstein Correlation Analysis for Movie Retrieval
Xueya Zhang, Tong Zhang, Xiaobin Hong +2
Movie graphs play an important role to bridge heterogenous modalities of videos and texts in human-centric retrieval. In this work, we propose Graph Wasserstein Correlation Analysi…
Graph Inference Learning for Semi-supervised Classification
Chunyan Xu, Zhen Cui, Xiaobin Hong +3
In this work, we address semi-supervised classification of graph data, where the categories of those unlabeled nodes are inferred from labeled nodes as well as graph structures. Re…
Dual-Attention Graph Convolutional Network
Xueya Zhang, Tong Zhang, Wenting Zhao +2
Graph convolutional networks (GCNs) have shown the powerful ability in text structure representation and effectively facilitate the task of text classification. However, challenges…
A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition
Yang Li, Wenming Zheng, Lei Wang +5
The neuroscience study has revealed the discrepancy of emotion expression between left and right hemispheres of human brain. Inspired by this study, in this paper, we propose a nov…