
Tackling OverSmoothing for General Graph Convolutional Networks
Increasing the depth of Graph Convolutional Networks (GCN), which in pri...
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Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels?
Graph Identification (GI) has long been researched in graph learning and...
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Dense Regression Network for Video Grounding
We address the problem of video grounding from natural language queries....
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Spectral Graph Attention Network
Variants of Graph Neural Networks (GNNs) for representation learning hav...
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Reusing Discriminators for Encoding Towards Unsupervised ImagetoImage Translation
Unsupervised imagetoimage translation is a central task in computer vi...
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Graph Representation Learning via Graphical Mutual Information Maximization
The richness in the content of various information networks such as soci...
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Rumor Detection on Social Media with BiDirectional Graph Convolutional Networks
Social media has been developing rapidly in public due to its nature of ...
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Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance
In this paper, we study Reinforcement Learning from Demonstrations (RLfD...
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Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
This paper studies Learning from Observations (LfO) for imitation learni...
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Graph Convolutional Networks for Temporal Action Localization
Most stateoftheart action localization systems process each action pr...
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A Fast and Accurate OneStage Approach to Visual Grounding
We propose a simple, fast, and accurate onestage approach to visual gro...
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A Restricted Blackbox Adversarial Framework Towards Attacking Graph Embedding Models
With the great success of graph embedding model on both academic and ind...
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The General Blackbox Attack Method for Graph Neural Networks
With the great success of Graph Neural Networks (GNNs) towards represent...
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DropEdge: Towards the Very Deep Graph Convolutional Networks for Node Classification
Existing Graph Convolutional Networks (GCNs) are shallowthe number of...
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The Truly Deep Graph Convolutional Networks for Node Classification
Existing Graph Convolutional Networks (GCNs) are shallowthe number of...
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Label Aware Graph Convolutional Network  Not All Edges Deserve Your Attention
Graph classification is practically important in many domains. To solve ...
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Unsupervised Adversarial Graph Alignment with Graph Embedding
Graph alignment, also known as network alignment, is a fundamental task ...
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Adversarial Representation Learning on LargeScale Bipartite Graphs
Graph representation on largescale bipartite graphs is central for a va...
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Neural Collaborative Subspace Clustering
We introduce the Neural Collaborative Subspace Clustering, a neural mode...
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SemiSupervised Graph Classification: A Hierarchical Graph Perspective
Node classification and graph classification are two graph learning prob...
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Weakly Supervised Dense Event Captioning in Videos
Dense event captioning aims to detect and describe all events of interes...
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Progressive Feature Alignment for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) transfers knowledge from a labelri...
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Adaptive Sampling Towards Fast Graph Representation Learning
Graph Convolutional Networks (GCNs) have become a crucial tool on learni...
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Deep Feature Pyramid Reconfiguration for Object Detection
Stateoftheart object detectors usually learn multiscale representati...
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Controllable ImagetoVideo Translation: A Case Study on Facial Expression Generation
The recent advances in deep learning have made it possible to generate p...
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EndtoEnd Learning of Motion Representation for Video Understanding
Despite the recent success of endtoend learned representations, handc...
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Generalized ZeroShot Learning for Action Recognition with WebScale Video Data
Action recognition in surveillance video makes our life safer by detecti...
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Analyzing Linear Dynamical Systems: From Modeling to Coding and Learning
Encoding timeseries with Linear Dynamical Systems (LDSs) leads to rich ...
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Wenbing Huang
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