Search Results for author: Wenbing Huang

Found 54 papers, 22 papers with code

Multimodal Token Fusion for Vision Transformers

no code implementations19 Apr 2022 Yikai Wang, Xinghao Chen, Lele Cao, Wenbing Huang, Fuchun Sun, Yunhe Wang

Many adaptations of transformers have emerged to address the single-modal vision tasks, where self-attention modules are stacked to handle input sources like images.

3D Object Detection Image-to-Image Translation +1

Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation

1 code implementation15 Mar 2022 Runfa Chen, Yu Rong, Shangmin Guo, Jiaqi Han, Fuchun Sun, Tingyang Xu, Wenbing Huang

After the great success of Vision Transformer variants (ViTs) in computer vision, it has also demonstrated great potential in domain adaptive semantic segmentation.

Synthetic-to-Real Translation Unsupervised Domain Adaptation

Equivariant Graph Mechanics Networks with Constraints

1 code implementation12 Mar 2022 Wenbing Huang, Jiaqi Han, Yu Rong, Tingyang Xu, Fuchun Sun, Junzhou Huang

The core of GMN is that it represents, by generalized coordinates, the forward kinematics information (positions and velocities) of a structural object.

Sound Adversarial Audio-Visual Navigation

1 code implementation ICLR 2022 Yinfeng Yu, Wenbing Huang, Fuchun Sun, Changan Chen, Yikai Wang, Xiaohong Liu

In this work, we design an acoustically complex environment in which, besides the target sound, there exists a sound attacker playing a zero-sum game with the agent.

Visual Navigation

Equivariant Graph Hierarchy-Based Neural Networks

no code implementations22 Feb 2022 Jiaqi Han, Yu Rong, Tingyang Xu, Fuchun Sun, Wenbing Huang

Equivariant Graph neural Networks (EGNs) are powerful in characterizing the dynamics of multi-body physical systems.

Transformer for Graphs: An Overview from Architecture Perspective

1 code implementation17 Feb 2022 Erxue Min, Runfa Chen, Yatao Bian, Tingyang Xu, Kangfei Zhao, Wenbing Huang, Peilin Zhao, Junzhou Huang, Sophia Ananiadou, Yu Rong

In this survey, we provide a comprehensive review of various Graph Transformer models from the architectural design perspective.

Geometrically Equivariant Graph Neural Networks: A Survey

no code implementations15 Feb 2022 Jiaqi Han, Yu Rong, Tingyang Xu, Wenbing Huang

Many scientific problems require to process data in the form of geometric graphs.

Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction

no code implementations4 Dec 2021 Yikai Wang, Wenbing Huang, Fuchun Sun, Fengxiang He, DaCheng Tao

For the application of dense image prediction, the validity of CEN is tested by four different scenarios: multimodal fusion, cycle multimodal fusion, multitask learning, and multimodal multitask learning.

Semantic Segmentation

Graph Convolutional Module for Temporal Action Localization in Videos

no code implementations1 Dec 2021 Runhao Zeng, Wenbing Huang, Mingkui Tan, Yu Rong, Peilin Zhao, Junzhou Huang, Chuang Gan

To this end, we propose a general graph convolutional module (GCM) that can be easily plugged into existing action localization methods, including two-stage and one-stage paradigms.

Action Recognition

Weakly Supervised Graph Clustering

no code implementations29 Sep 2021 Tian Bian, Tingyang Xu, Yu Rong, Wenbing Huang, Xi Xiao, Peilin Zhao, Junzhou Huang, Hong Cheng

Graph Clustering, which clusters the nodes of a graph given its collection of node features and edge connections in an unsupervised manner, has long been researched in graph learning and is essential in certain applications.

Graph Clustering Graph Learning

PI-GNN: Towards Robust Semi-Supervised Node Classification against Noisy Labels

no code implementations29 Sep 2021 Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Junzhou Huang

Semi-supervised node classification on graphs is a fundamental problem in graph mining that uses a small set of labeled nodes and many unlabeled nodes for training, so that its performance is quite sensitive to the quality of the node labels.

Graph Mining Node Classification

Constrained Graph Mechanics Networks

no code implementations ICLR 2022 Wenbing Huang, Jiaqi Han, Yu Rong, Tingyang Xu, Fuchun Sun, Junzhou Huang

In this manner, the geometrical constraints are implicitly and naturally encoded in the forward kinematics.

Elastic Tactile Simulation Towards Tactile-Visual Perception

1 code implementation11 Aug 2021 Yikai Wang, Wenbing Huang, Bin Fang, Fuchun Sun, Chang Li

By contrast, EIP models the tactile sensor as a group of coordinated particles, and the elastic property is applied to regulate the deformation of particles during contact.

PI-GNN: A Novel Perspective on Semi-Supervised Node Classification against Noisy Labels

no code implementations14 Jun 2021 Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Junzhou Huang

Semi-supervised node classification, as a fundamental problem in graph learning, leverages unlabeled nodes along with a small portion of labeled nodes for training.

Graph Learning Node Classification

Adversarial Option-Aware Hierarchical Imitation Learning

1 code implementation10 Jun 2021 Mingxuan Jing, Wenbing Huang, Fuchun Sun, Xiaojian Ma, Tao Kong, Chuang Gan, Lei LI

In particular, we propose an Expectation-Maximization(EM)-style algorithm: an E-step that samples the options of expert conditioned on the current learned policy, and an M-step that updates the low- and high-level policies of agent simultaneously to minimize the newly proposed option-occupancy measurement between the expert and the agent.

Imitation Learning

Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge

no code implementations26 May 2021 Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Xin Wang, Wenwu Zhu, Junzhou Huang

We investigate the theoretical connections between graph signal processing and graph embedding models and formulate the graph embedding model as a general graph signal process with a corresponding graph filter.

Adversarial Attack Graph Embedding +1

Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training

no code implementations NeurIPS 2021 Xueyi Liu, Yu Rong, Tingyang Xu, Fuchun Sun, Wenbing Huang, Junzhou Huang

To remedy this issue, we propose to select positive graph instances directly from existing graphs in the training set, which ultimately maintains the legality and similarity to the target graphs.

Contrastive Learning Graph Classification +1

Elastic Interaction of Particles for Robotic Tactile Simulation

no code implementations23 Nov 2020 Yikai Wang, Wenbing Huang, Bin Fang, Fuchun Sun

At its core, EIP models the tactile sensor as a group of coordinated particles, and the elastic theory is applied to regulate the deformation of particles during the contact process.

Deep Multimodal Fusion by Channel Exchanging

1 code implementation NeurIPS 2020 Yikai Wang, Wenbing Huang, Fuchun Sun, Tingyang Xu, Yu Rong, Junzhou Huang

Deep multimodal fusion by using multiple sources of data for classification or regression has exhibited a clear advantage over the unimodal counterpart on various applications.

Image-to-Image Translation Semantic Segmentation +1

Tackling Over-Smoothing for General Graph Convolutional Networks

no code implementations22 Aug 2020 Wenbing Huang, Yu Rong, Tingyang Xu, Fuchun Sun, Junzhou Huang

Increasing the depth of GCN, which is expected to permit more expressivity, is shown to incur performance detriment especially on node classification.

Node Classification

Towards Purely Unsupervised Disentanglement of Appearance and Shape for Person Images Generation

no code implementations26 Jul 2020 Hongtao Yang, Tong Zhang, Wenbing Huang, Xuming He, Fatih Porikli

To be clear, in this paper, we refer unsupervised learning as learning without task-specific human annotations, pairs or any form of weak supervision.)

Disentanglement

Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels?

no code implementations12 Jul 2020 Tian Bian, Xi Xiao, Tingyang Xu, Yu Rong, Wenbing Huang, Peilin Zhao, Junzhou Huang

Upon a formal discussion of the variants of IGI, we choose a particular case study of node clustering by making use of the graph labels and node features, with an assistance of a hierarchical graph that further characterizes the connections between different graphs.

Community Detection Graph Attention +2

Self-Supervised Graph Transformer on Large-Scale Molecular Data

1 code implementation NeurIPS 2020 Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying WEI, Wenbing Huang, Junzhou Huang

We pre-train GROVER with 100 million parameters on 10 million unlabelled molecules -- the biggest GNN and the largest training dataset in molecular representation learning.

Molecular Property Prediction Representation Learning

Multi-View Graph Neural Networks for Molecular Property Prediction

no code implementations17 May 2020 Hehuan Ma, Yatao Bian, Yu Rong, Wenbing Huang, Tingyang Xu, Weiyang Xie, Geyan Ye, Junzhou Huang

Guided by this observation, we present Multi-View Graph Neural Network (MV-GNN), a multi-view message passing architecture to enable more accurate predictions of molecular properties.

Drug Discovery Molecular Property Prediction

Dense Regression Network for Video Grounding

1 code implementation CVPR 2020 Runhao Zeng, Haoming Xu, Wenbing Huang, Peihao Chen, Mingkui Tan, Chuang Gan

The key idea of this paper is to use the distances between the frame within the ground truth and the starting (ending) frame as dense supervisions to improve the video grounding accuracy.

Frame

Spectral Graph Attention Network with Fast Eigen-approximation

no code implementations16 Mar 2020 Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Somayeh Sojoudi, Junzhou Huang, Wenwu Zhu

In this paper, we first introduce the attention mechanism in the spectral domain of graphs and present Spectral Graph Attention Network (SpGAT) that learns representations for different frequency components regarding weighted filters and graph wavelets bases.

Graph Attention Node Classification +1

Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation

2 code implementations CVPR 2020 Runfa Chen, Wenbing Huang, Binghui Huang, Fuchun Sun, Bin Fang

The proposed architecture, termed as NICE-GAN, exhibits two advantageous patterns over previous approaches: First, it is more compact since no independent encoding component is required; Second, this plug-in encoder is directly trained by the adversary loss, making it more informative and trained more effectively if a multi-scale discriminator is applied.

Translation Unsupervised Image-To-Image Translation

Graph Representation Learning via Graphical Mutual Information Maximization

1 code implementation4 Feb 2020 Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu, Junzhou Huang

The richness in the content of various information networks such as social networks and communication networks provides the unprecedented potential for learning high-quality expressive representations without external supervision.

Graph Representation Learning Link Prediction +2

Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks

1 code implementation17 Jan 2020 Tian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao, Wenbing Huang, Yu Rong, Junzhou Huang

Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge.

Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance

no code implementations16 Nov 2019 Mingxuan Jing, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Chao Yang, Bin Fang, Huaping Liu

In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations.

reinforcement-learning

Graph Convolutional Networks for Temporal Action Localization

1 code implementation ICCV 2019 Runhao Zeng, Wenbing Huang, Mingkui Tan, Yu Rong, Peilin Zhao, Junzhou Huang, Chuang Gan

Then we apply the GCNs over the graph to model the relations among different proposals and learn powerful representations for the action classification and localization.

Action Classification Temporal Action Localization

A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models

1 code implementation4 Aug 2019 Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, Junzhou Huang

To this end, we begin by investigating the theoretical connections between graph signal processing and graph embedding models in a principled way and formulate the graph embedding model as a general graph signal process with corresponding graph filter.

Adversarial Attack Graph Embedding +1

DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

4 code implementations ICLR 2020 Yu Rong, Wenbing Huang, Tingyang Xu, Junzhou Huang

\emph{Over-fitting} and \emph{over-smoothing} are two main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification.

Classification General Classification +1

Label-Aware Graph Convolutional Networks

no code implementations10 Jul 2019 Hao Chen, Yue Xu, Feiran Huang, Zengde Deng, Wenbing Huang, Senzhang Wang, Peng He, Zhoujun Li

In this paper, we consider the problem of node classification and propose the Label-Aware Graph Convolutional Network (LAGCN) framework which can directly identify valuable neighbors to enhance the performance of existing GCN models.

Classification General Classification +3

Unsupervised Adversarial Graph Alignment with Graph Embedding

no code implementations1 Jul 2019 Chaoqi Chen, Weiping Xie, Tingyang Xu, Yu Rong, Wenbing Huang, Xinghao Ding, Yue Huang, Junzhou Huang

In this paper, we propose an Unsupervised Adversarial Graph Alignment (UAGA) framework to learn a cross-graph alignment between two embedding spaces of different graphs in a fully unsupervised fashion (\emph{i. e.,} no existing anchor links and no users' personal profile or attribute information is available).

Graph Embedding Link Prediction

Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs

1 code implementation27 Jun 2019 Chaoyang He, Tian Xie, Yu Rong, Wenbing Huang, Junzhou Huang, Xiang Ren, Cyrus Shahabi

Existing techniques either cannot be scaled to large-scale bipartite graphs that have limited labels or cannot exploit the unique structure of bipartite graphs, which have distinct node features in two domains.

Recommendation Systems Representation Learning

Neural Collaborative Subspace Clustering

no code implementations24 Apr 2019 Tong Zhang, Pan Ji, Mehrtash Harandi, Wenbing Huang, Hongdong Li

We introduce the Neural Collaborative Subspace Clustering, a neural model that discovers clusters of data points drawn from a union of low-dimensional subspaces.

Semi-Supervised Graph Classification: A Hierarchical Graph Perspective

1 code implementation10 Apr 2019 Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang, Junzhou Huang

We study the node classification problem in the hierarchical graph where a `node' is a graph instance, e. g., a user group in the above example.

Classification General Classification +4

Weakly Supervised Dense Event Captioning in Videos

no code implementations NeurIPS 2018 Xuguang Duan, Wenbing Huang, Chuang Gan, Jingdong Wang, Wenwu Zhu, Junzhou Huang

Dense event captioning aims to detect and describe all events of interest contained in a video.

PocketFlow: An Automated Framework for Compressing and Accelerating Deep Neural Networks

1 code implementation NIPS Workshop CDNNRIA 2018 Jiaxiang Wu, Yao Zhang, Haoli Bai, Huasong Zhong, Jinlong Hou, Wei Liu, Wenbing Huang, Junzhou Huang

Deep neural networks are widely used in various domains, but the prohibitive computational complexity prevents their deployment on mobile devices.

Model Compression

Deep Feature Pyramid Reconfiguration for Object Detection

no code implementations ECCV 2018 Tao Kong, Fuchun Sun, Wenbing Huang, Huaping Liu

In this paper, we begin by investigating current feature pyramids solutions, and then reformulate the feature pyramid construction as the feature reconfiguration process.

Object Detection

Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation

no code implementations9 Aug 2018 Lijie Fan, Wenbing Huang, Chuang Gan, Junzhou Huang, Boqing Gong

The recent advances in deep learning have made it possible to generate photo-realistic images by using neural networks and even to extrapolate video frames from an input video clip.

Image-to-Image Translation Translation +1

Task Transfer by Preference-Based Cost Learning

no code implementations12 May 2018 Mingxuan Jing, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Huaping Liu

The goal of task transfer in reinforcement learning is migrating the action policy of an agent to the target task from the source task.

End-to-End Learning of Motion Representation for Video Understanding

1 code implementation CVPR 2018 Lijie Fan, Wenbing Huang, Chuang Gan, Stefano Ermon, Boqing Gong, Junzhou Huang

Despite the recent success of end-to-end learned representations, hand-crafted optical flow features are still widely used in video analysis tasks.

Action Recognition Optical Flow Estimation +1

Generalized Zero-Shot Learning for Action Recognition with Web-Scale Video Data

no code implementations20 Oct 2017 Kun Liu, Wu Liu, Huadong Ma, Wenbing Huang, Xiongxiong Dong

Motivated by this, we study the task of action recognition in surveillance video under a more realistic \emph{generalized zero-shot setting}, where testing data contains both seen and unseen classes.

Action Recognition Generalized Zero-Shot Learning

Analyzing Linear Dynamical Systems: From Modeling to Coding and Learning

1 code implementation3 Aug 2016 Wenbing Huang, Fuchun Sun, Lele Cao, Mehrtash Harandi

We then devise efficient algorithms to perform sparse coding and dictionary learning on the space of infinite-dimensional subspaces.

Dictionary Learning General Classification +4

Sparse Coding and Dictionary Learning With Linear Dynamical Systems

no code implementations CVPR 2016 Wenbing Huang, Fuchun Sun, Lele Cao, Deli Zhao, Huaping Liu, Mehrtash Harandi

To enhance the performance of LDSs, in this paper, we address the challenging issue of performing sparse coding on the space of LDSs, where both data and dictionary atoms are LDSs.

Dictionary Learning Video Classification

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