Search Results for author: Yu Rong

Found 62 papers, 28 papers with code

Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs

no code implementations31 Mar 2022 Jiying Zhang, Fuyang Li, Xi Xiao, Tingyang Xu, Yu Rong, Junzhou Huang, Yatao Bian

As a powerful tool for modeling complex relationships, hypergraphs are gaining popularity from the graph learning community.

Graph Learning

Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport

1 code implementation20 Mar 2022 Jiying Zhang, Xi Xiao, Long-Kai Huang, Yu Rong, Yatao Bian

In this paper, we present a novel optimal transport-based fine-tuning framework called GTOT-Tuning, namely, Graph Topology induced Optimal Transport fine-Tuning, for GNN style backbones.

Graph Classification Graph Learning +2

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.

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.

Not All Low-Pass Filters are Robust in Graph Convolutional Networks

1 code implementation NeurIPS 2021 Heng Chang, Yu Rong, Tingyang Xu, Yatao Bian, Shiji Zhou, Xin Wang, Junzhou Huang, Wenwu Zhu

Graph Convolutional Networks (GCNs) are promising deep learning approaches in learning representations for graph-structured data.

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

Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements

no code implementations1 Nov 2021 Yu Rong, Jingbo Wang, Ziwei Liu, Chen Change Loy

In this paper, we make the first attempt to reconstruct 3D interacting hands from monocular single RGB images.

3D Reconstruction

VoteHMR: Occlusion-Aware Voting Network for Robust 3D Human Mesh Recovery from Partial Point Clouds

no code implementations17 Oct 2021 Guanze Liu, Yu Rong, Lu Sheng

3D human mesh recovery from point clouds is essential for various tasks, including AR/VR and human behavior understanding.

Human Mesh Recovery

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.

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

Local Augmentation for Graph Neural Networks

no code implementations8 Sep 2021 Songtao Liu, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, Dinghao Wu

Data augmentation has been widely used in image data and linguistic data but remains under-explored for Graph Neural Networks (GNNs).

FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration

1 code implementation13 Aug 2021 Yu Rong, Takaaki Shiratori, Hanbyul Joo

Most existing monocular 3D pose estimation approaches only focus on a single body part, neglecting the fact that the essential nuance of human motion is conveyed through a concert of subtle movements of face, hands, and body.

3D Human Reconstruction 3D Pose Estimation

Frustratingly Easy Transferability Estimation

no code implementations17 Jun 2021 Long-Kai Huang, Ying WEI, Yu Rong, Qiang Yang, Junzhou Huang

Transferability estimation has been an essential tool in selecting a pre-trained model and the layers in it for transfer learning, to transfer, so as to maximize the performance on a target task and prevent negative transfer.

Mutual Information Estimation Transfer Learning

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

Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising

no code implementations7 Jun 2021 Xiangyu Liu, Chuan Yu, Zhilin Zhang, Zhenzhe Zheng, Yu Rong, Hongtao Lv, Da Huo, YiQing Wang, Dagui Chen, Jian Xu, Fan Wu, Guihai Chen, Xiaoqiang Zhu

In e-commerce advertising, it is crucial to jointly consider various performance metrics, e. g., user experience, advertiser utility, and platform revenue.

Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning

no code implementations ICLR 2022 Yatao Bian, Yu Rong, Tingyang Xu, Jiaxiang Wu, Andreas Krause, Junzhou Huang

By running fixed point iteration for multiple steps, we achieve a trajectory of the valuations, among which we define the valuation with the best conceivable decoupling error as the Variational Index.

Variational Inference

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

Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation

1 code implementation ICCV 2021 Jinyu Yang, Chunyuan Li, Weizhi An, Hehuan Ma, Yuzhi Guo, Yu Rong, Peilin Zhao, Junzhou Huang

Recent studies imply that deep neural networks are vulnerable to adversarial examples -- inputs with a slight but intentional perturbation are incorrectly classified by the network.

Semantic Segmentation Unsupervised Domain Adaptation

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

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks

1 code implementation14 Apr 2021 Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Carl Yang, Han Xie, Lichao Sun, Lifang He, Liangwei Yang, Philip S. Yu, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, Salman Avestimehr

FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support.

Federated Learning Molecular Property Prediction

Diversified Multiscale Graph Learning with Graph Self-Correction

no code implementations17 Mar 2021 Yuzhao Chen, Yatao Bian, Jiying Zhang, Xi Xiao, Tingyang Xu, Yu Rong, Junzhou Huang

Though the multiscale graph learning techniques have enabled advanced feature extraction frameworks, the classic ensemble strategy may show inferior performance while encountering the high homogeneity of the learnt representation, which is caused by the nature of existing graph pooling methods.

Ensemble Learning Graph Classification +1

Learning Diverse-Structured Networks for Adversarial Robustness

1 code implementation3 Feb 2021 Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama

In adversarial training (AT), the main focus has been the objective and optimizer while the model has been less studied, so that the models being used are still those classic ones in standard training (ST).

Adversarial Robustness

Chasing the Tail in Monocular 3D Human Reconstruction with Prototype Memory

no code implementations29 Dec 2020 Yu Rong, Ziwei Liu, Chen Change Loy

The reason is that most of the current models perform regression based on a single human prototype, which is similar to common poses while far from the rare poses.

3D Human Reconstruction

Hierarchical Graph Capsule Network

1 code implementation16 Dec 2020 Jinyu Yang, Peilin Zhao, Yu Rong, Chaochao Yan, Chunyuan Li, Hehuan Ma, Junzhou Huang

Graph Neural Networks (GNNs) draw their strength from explicitly modeling the topological information of structured data.

Graph Classification

Non-Contact Vital Signs Detection with UAV-Borne Radars

no code implementations27 Nov 2020 Yu Rong, Richard M. Gutierrez, Kumar Vijay Mishra, Daniel W. Bliss

Aggregating radar measurements with the information from other sensors is broadening the applications of drones in life-critical situations.

Disaster Response

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

On Self-Distilling Graph Neural Network

no code implementations4 Nov 2020 Yuzhao Chen, Yatao Bian, Xi Xiao, Yu Rong, Tingyang Xu, Junzhou Huang

Furthermore, the inefficient training process of teacher-student knowledge distillation also impedes its applications in GNN models.

Graph Embedding Knowledge Distillation

Graph Information Bottleneck for Subgraph Recognition

1 code implementation ICLR 2021 Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, Ran He

In this paper, we propose a framework of Graph Information Bottleneck (GIB) for the subgraph recognition problem in deep graph learning.

Denoising Graph Classification +1

Dirichlet Graph Variational Autoencoder

1 code implementation NeurIPS 2020 Jia Li, Tomasyu Yu, Jiajin Li, Honglei Zhang, Kangfei Zhao, Yu Rong, Hong Cheng, Junzhou Huang

In this work, we present Dirichlet Graph Variational Autoencoder (DGVAE) with graph cluster memberships as latent factors.

Graph Clustering Graph Generation

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

FrankMocap: Fast Monocular 3D Hand and Body Motion Capture by Regression and Integration

1 code implementation19 Aug 2020 Yu Rong, Takaaki Shiratori, Hanbyul Joo

To construct FrankMocap, we build the state-of-the-art monocular 3D "hand" motion capture method by taking the hand part of the whole body parametric model (SMPL-X).

3D Hand Pose Estimation 3D Human Reconstruction +1

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

Towards Feature-free TSP Solver Selection: A Deep Learning Approach

no code implementations1 Jun 2020 Kangfei Zhao, Shengcai Liu, Yu Rong, Jeffrey Xu Yu

To solve TSP efficiently, in addition to developing new TSP solvers, it needs to find a per-instance solver for each TSP instance, which is known as the TSP solver selection problem.

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

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

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

Adversarial Attack on Community Detection by Hiding Individuals

1 code implementation22 Jan 2020 Jia Li, Honglei Zhang, Zhichao Han, Yu Rong, Hong Cheng, Junzhou Huang

It has been demonstrated that adversarial graphs, i. e., graphs with imperceptible perturbations added, can cause deep graph models to fail on node/graph classification tasks.

Adversarial Attack Community Detection +1

Graph Ordering: Towards the Optimal by Learning

no code implementations18 Jan 2020 Kangfei Zhao, Yu Rong, Jeffrey Xu Yu, Junzhou Huang, Hao Zhang

However, regardless of the fruitful progress, for some kind of graph applications, such as graph compression and edge partition, it is very hard to reduce them to some graph representation learning tasks.

Combinatorial Optimization Community Detection +3

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.

Octave Graph Convolutional Network

no code implementations25 Sep 2019 Heng Chang, Yu Rong, Somayeh Sojoudi, Junzhou Huang, Wenwu Zhu

Many variants of Graph Convolutional Networks (GCNs) for representation learning have been proposed recently and have achieved fruitful results in various domains.

Node Classification Representation 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

Delving Deep Into Hybrid Annotations for 3D Human Recovery in the Wild

1 code implementation ICCV 2019 Yu Rong, Ziwei Liu, Cheng Li, Kaidi Cao, Chen Change Loy

Specifically, we focus on the challenging task of in-the-wild 3D human recovery from single images when paired 3D annotations are not fully available.

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

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

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

On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches

no code implementations14 Jul 2018 Jie Liu, Yu Rong, Martin Takac, Junzhou Huang

This paper proposes a framework of L-BFGS based on the (approximate) second-order information with stochastic batches, as a novel approach to the finite-sum minimization problems.

Pose-Robust Face Recognition via Deep Residual Equivariant Mapping

1 code implementation CVPR 2018 Kaidi Cao, Yu Rong, Cheng Li, Xiaoou Tang, Chen Change Loy

However, many contemporary face recognition models still perform relatively poor in processing profile faces compared to frontal faces.

Face Identification Face Recognition +2

Cannot find the paper you are looking for? You can Submit a new open access paper.