Search Results for author: Xiang Wang

Found 70 papers, 40 papers with code

Training Free Graph Neural Networks for Graph Matching

1 code implementation14 Jan 2022 Zhiyuan Liu, Yixin Cao, Fuli Feng, Xiang Wang, Xindi Shang, Jie Tang, Kenji Kawaguchi, Tat-Seng Chua

We present TFGM (Training Free Graph Matching), a framework to boost the performance of Graph Neural Networks (GNNs) based graph matching without training.

Entity Alignment Graph Matching +1

Deconfounded Training for Graph Neural Networks

no code implementations30 Dec 2021 Yongduo Sui, Xiang Wang, Jiancan Wu, Xiangnan He, Tat-Seng Chua

However, this training paradigm is prone to capture the spurious correlations between the trivial subgraph and the label.

Graph Attention

Towards Multi-Grained Explainability for Graph Neural Networks

1 code implementation NeurIPS 2021 Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He, Tat-Seng Chua

A performant paradigm towards multi-grained explainability is until-now lacking and thus a focus of our work.

FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows

2 code implementations15 Nov 2021 Jiawei Yu, Ye Zheng, Xiang Wang, Wei Li, Yushuang Wu, Rui Zhao, Liwei Wu

However, current methods can not effectively map image features to a tractable base distribution and ignore the relationship between local and global features which are important to identify anomalies.

 Ranked #1 on Anomaly Detection on MVTec AD (using extra training data)

Unsupervised Anomaly Detection

Automated Pulmonary Embolism Detection from CTPA Images Using an End-to-End Convolutional Neural Network

no code implementations10 Nov 2021 Yi Lin, Jianchao Su, Xiang Wang, Xiang Li, Jingen Liu, Kwang-Ting Cheng, Xin Yang

We have evaluated our approach using the 20 CTPA test dataset from the PE challenge, achieving a sensitivity of 78. 9%, 80. 7% and 80. 7% at 2 false positives per volume at 0mm, 2mm and 5mm localization error, which is superior to the state-of-the-art methods.

Pulmonary Embolism Detection

Towards Understanding the Data Dependency of Mixup-style Training

1 code implementation14 Oct 2021 Muthu Chidambaram, Xiang Wang, Yuzheng Hu, Chenwei Wu, Rong Ge

Despite seeing very few true data points during training, models trained using Mixup seem to still minimize the original empirical risk and exhibit better generalization and robustness on various tasks when compared to standard training.

Towards Demystifying Representation Learning with Non-contrastive Self-supervision

1 code implementation11 Oct 2021 Xiang Wang, Xinlei Chen, Simon S. Du, Yuandong Tian

Non-contrastive methods of self-supervised learning (such as BYOL and SimSiam) learn representations by minimizing the distance between two views of the same image.

Representation Learning Self-Supervised Learning

Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization

no code implementations9 Oct 2021 Ye Zheng, Xiang Wang, Rui Deng, Tianpeng Bao, Rui Zhao, Liwei Wu

To facilitate the learning with only normal images, we propose a new pretext task called non-contrastive learning for the fine alignment stage.

Ranked #7 on Anomaly Detection on MVTec AD (using extra training data)

Contrastive Learning Unsupervised Anomaly Detection

Time-aware Path Reasoning on Knowledge Graph for Recommendation

no code implementations5 Aug 2021 Yuyue Zhao, Xiang Wang, Jiawei Chen, Wei Tang, Yashen Wang, Xiangnan He, Haiyong Xie

In this work, we propose a novel Time-aware Path reasoning for Recommendation (TPRec for short) method, which leverages the potential of temporal information to offer better recommendation with plausible explanations.

Relation Extraction

Exploring Lottery Ticket Hypothesis in Media Recommender Systems

1 code implementation2 Aug 2021 Yanfang Wang, Yongduo Sui, Xiang Wang, Zhenguang Liu, Xiangnan He

We get inspirations from the recently proposed lottery ticket hypothesis (LTH), which argues that the dense and over-parameterized model contains a much smaller and sparser sub-model that can reach comparable performance to the full model.

Recommendation Systems Representation Learning

Visual Boundary Knowledge Translation for Foreground Segmentation

1 code implementation1 Aug 2021 Zunlei Feng, Lechao Cheng, Xinchao Wang, Xiang Wang, Yajie Liu, Xiangtong Du, Mingli Song

To this end, we propose a Translation Segmentation Network (Trans-Net), which comprises a segmentation network and two boundary discriminators.

Semantic Segmentation Translation

OadTR: Online Action Detection with Transformers

1 code implementation ICCV 2021 Xiang Wang, Shiwei Zhang, Zhiwu Qing, Yuanjie Shao, Zhengrong Zuo, Changxin Gao, Nong Sang

Most recent approaches for online action detection tend to apply Recurrent Neural Network (RNN) to capture long-range temporal structure.

Action Detection

A Stronger Baseline for Ego-Centric Action Detection

1 code implementation13 Jun 2021 Zhiwu Qing, Ziyuan Huang, Xiang Wang, Yutong Feng, Shiwei Zhang, Jianwen Jiang, Mingqian Tang, Changxin Gao, Marcelo H. Ang Jr, Nong Sang

This technical report analyzes an egocentric video action detection method we used in the 2021 EPIC-KITCHENS-100 competition hosted in CVPR2021 Workshop.

Action Detection

Understanding Deflation Process in Over-parametrized Tensor Decomposition

no code implementations NeurIPS 2021 Rong Ge, Yunwei Ren, Xiang Wang, Mo Zhou

In this paper we study the training dynamics for gradient flow on over-parametrized tensor decomposition problems.

Tensor Decomposition

Deconfounded Recommendation for Alleviating Bias Amplification

1 code implementation22 May 2021 Wenjie Wang, Fuli Feng, Xiangnan He, Xiang Wang, Tat-Seng Chua

In this work, we scrutinize the cause-effect factors for bias amplification, identifying the main reason lies in the confounder effect of imbalanced item distribution on user representation and prediction score.

Fairness Recommendation Systems

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

1 code implementation27 Apr 2021 Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks.

Collaborative Filtering Sequential Recommendation

A-FMI: Learning Attributions from Deep Networks via Feature Map Importance

no code implementations12 Apr 2021 An Zhang, Xiang Wang, Chengfang Fang, Jie Shi, Tat-Seng Chua, Zehua Chen

Gradient-based attribution methods can aid in the understanding of convolutional neural networks (CNNs).

Temporal Context Aggregation Network for Temporal Action Proposal Refinement

1 code implementation CVPR 2021 Zhiwu Qing, Haisheng Su, Weihao Gan, Dongliang Wang, Wei Wu, Xiang Wang, Yu Qiao, Junjie Yan, Changxin Gao, Nong Sang

In this paper, we propose Temporal Context Aggregation Network (TCANet) to generate high-quality action proposals through "local and global" temporal context aggregation and complementary as well as progressive boundary refinement.

Action Detection Temporal Action Proposal Generation +1

Learning Intents behind Interactions with Knowledge Graph for Recommendation

1 code implementation14 Feb 2021 Xiang Wang, Tinglin Huang, Dingxian Wang, Yancheng Yuan, Zhenguang Liu, Xiangnan He, Tat-Seng Chua

In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN).

Recommendation Systems

Causal Screening to Interpret Graph Neural Networks

no code implementations1 Jan 2021 Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He, Tat-Seng Chua

In this work, we focus on the causal interpretability in GNNs and propose a method, Causal Screening, from the perspective of cause-effect.

Beyond Lazy Training for Over-parameterized Tensor Decomposition

no code implementations NeurIPS 2020 Xiang Wang, Chenwei Wu, Jason D. Lee, Tengyu Ma, Rong Ge

We show that in a lazy training regime (similar to the NTK regime for neural networks) one needs at least $m = \Omega(d^{l-1})$, while a variant of gradient descent can find an approximate tensor when $m = O^*(r^{2. 5l}\log d)$.

Tensor Decomposition

Self-supervised Graph Learning for Recommendation

1 code implementation21 Oct 2020 Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, Xing Xie

In this work, we explore self-supervised learning on user-item graph, so as to improve the accuracy and robustness of GCNs for recommendation.

Graph Learning Representation Learning +1

Multi-Level Temporal Pyramid Network for Action Detection

no code implementations7 Aug 2020 Xiang Wang, Changxin Gao, Shiwei Zhang, Nong Sang

By this means, the proposed MLTPN can learn rich and discriminative features for different action instances with different durations.

Action Detection

Disentangled Graph Collaborative Filtering

2 code implementations3 Jul 2020 Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, Tat-Seng Chua

Such uniform approach to model user interests easily results in suboptimal representations, failing to model diverse relationships and disentangle user intents in representations.

Collaborative Filtering

Interactive Path Reasoning on Graph for Conversational Recommendation

no code implementations1 Jul 2020 Wenqiang Lei, Gangyi Zhang, Xiangnan He, Yisong Miao, Xiang Wang, Liang Chen, Tat-Seng Chua

Traditional recommendation systems estimate user preference on items from past interaction history, thus suffering from the limitations of obtaining fine-grained and dynamic user preference.

Recommendation Systems

Guarantees for Tuning the Step Size using a Learning-to-Learn Approach

1 code implementation30 Jun 2020 Xiang Wang, Shuai Yuan, Chenwei Wu, Rong Ge

Solving this problem using a learning-to-learn approach -- using meta-gradient descent on a meta-objective based on the trajectory that the optimizer generates -- was recently shown to be effective.

Temporal Fusion Network for Temporal Action Localization:Submission to ActivityNet Challenge 2020 (Task E)

no code implementations13 Jun 2020 Zhiwu Qing, Xiang Wang, Yongpeng Sang, Changxin Gao, Shiwei Zhang, Nong Sang

This technical report analyzes a temporal action localization method we used in the HACS competition which is hosted in Activitynet Challenge 2020. The goal of our task is to locate the start time and end time of the action in the untrimmed video, and predict action category. Firstly, we utilize the video-level feature information to train multiple video-level action classification models.

Action Classification Temporal Action Localization

Hierarchical Fashion Graph Network for Personalized Outfit Recommendation

1 code implementation26 May 2020 Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, Tat-Seng Chua

Fashion outfit recommendation has attracted increasing attentions from online shopping services and fashion communities. Distinct from other scenarios (e. g., social networking or content sharing) which recommend a single item (e. g., a friend or picture) to a user, outfit recommendation predicts user preference on a set of well-matched fashion items. Hence, performing high-quality personalized outfit recommendation should satisfy two requirements -- 1) the nice compatibility of fashion items and 2) the consistence with user preference.

Visual Perception Model for Rapid and Adaptive Low-light Image Enhancement

1 code implementation15 May 2020 Xiaoxiao Li, Xiaopeng Guo, Liye Mei, Mingyu Shang, Jie Gao, Maojing Shu, Xiang Wang

The core of VP model is to decompose the light source into light intensity and light spatial distribution to describe the perception process of HVS, offering refinement estimation of illumination and reflectance.

Low-Light Image Enhancement

XTDrone: A Customizable Multi-Rotor UAVs Simulation Platform

1 code implementation21 Mar 2020 Kun Xiao, Shaochang Tan, Guohui Wang, Xueyan An, Xiang Wang, Xiangke Wang

A customizable multi-rotor UAVs simulation platform based on ROS, Gazebo and PX4 is presented.


Reinforced Negative Sampling over Knowledge Graph for Recommendation

1 code implementation12 Mar 2020 Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Meng Wang, Tat-Seng Chua

Properly handling missing data is a fundamental challenge in recommendation.

Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning

no code implementations19 Feb 2020 Xiang Wang, Sifei Liu, Huimin Ma, Ming-Hsuan Yang

In this paper, we propose an iterative algorithm to learn such pairwise relations, which consists of two branches, a unary segmentation network which learns the label probabilities for each pixel, and a pairwise affinity network which learns affinity matrix and refines the probability map generated from the unary network.

Weakly-Supervised Semantic Segmentation

Bilinear Graph Neural Network with Neighbor Interactions

1 code implementation10 Feb 2020 Hongmin Zhu, Fuli Feng, Xiangnan He, Xiang Wang, Yan Li, Kai Zheng, Yongdong Zhang

We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes.

General Classification Node Classification

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

12 code implementations6 Feb 2020 Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, Meng Wang

We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering.

Collaborative Filtering Graph Classification +1

Graph Convolution Machine for Context-aware Recommender System

1 code implementation30 Jan 2020 Jiancan Wu, Xiangnan He, Xiang Wang, Qifan Wang, Weijian Chen, Jianxun Lian, Xing Xie

The encoder projects users, items, and contexts into embedding vectors, which are passed to the GC layers that refine user and item embeddings with context-aware graph convolutions on user-item graph.

Collaborative Filtering Recommendation Systems

Multiple Sample Clustering

no code implementations22 Oct 2019 Xiang Wang, Tie Liu

The clustering algorithms that view each object data as a single sample drawn from a certain distribution, Gaussian distribution, for example, has been a hot topic for decades.

Quantum-enhanced least-square support vector machine: simplified quantum algorithm and sparse solutions

no code implementations5 Aug 2019 Jie Lin, Dan-Bo Zhang, Shuo Zhang, Xiang Wang, Tan Li, Wan-su Bao

We also incorporate kernel methods into the above quantum algorithms, which uses both exponential growth Hilbert space of qubits and infinite dimensionality of continuous variable for quantum feature maps.

Neural Graph Collaborative Filtering

14 code implementations20 May 2019 Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua

Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF.

Collaborative Filtering Link Prediction +1

KGAT: Knowledge Graph Attention Network for Recommendation

5 code implementations20 May 2019 Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account.

Graph Attention Knowledge Graphs +2

Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization

no code implementations1 May 2019 Rong Ge, Zhize Li, Wei-Yao Wang, Xiang Wang

Variance reduction techniques like SVRG provide simple and fast algorithms for optimizing a convex finite-sum objective.

A Survey on Face Data Augmentation

no code implementations26 Apr 2019 Xiang Wang, Kai Wang, Shiguo Lian

The quality and size of training set have great impact on the results of deep learning-based face related tasks.

Data Augmentation

A Unified Framework for Mutual Improvement of SLAM and Semantic Segmentation

no code implementations25 Dec 2018 Kai Wang, Yimin Lin, Luowei Wang, Liming Han, Minjie Hua, Xiang Wang, Shiguo Lian, Bill Huang

This paper presents a novel framework for simultaneously implementing localization and segmentation, which are two of the most important vision-based tasks for robotics.

Semantic Segmentation

Explainable Reasoning over Knowledge Graphs for Recommendation

2 code implementations12 Nov 2018 Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, Tat-Seng Chua

Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user's interest.

Knowledge Graphs Recommendation Systems

Deep Item-based Collaborative Filtering for Top-N Recommendation

1 code implementation11 Nov 2018 Feng Xue, Xiangnan He, Xiang Wang, Jiandong Xu, Kai Liu, Richang Hong

In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items.

Collaborative Filtering Decision Making +1

Learning Two-layer Neural Networks with Symmetric Inputs

no code implementations ICLR 2019 Rong Ge, Rohith Kuditipudi, Zhize Li, Xiang Wang

We give a new algorithm for learning a two-layer neural network under a general class of input distributions.

Temporal Relational Ranking for Stock Prediction

2 code implementations25 Sep 2018 Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo, Yiqun Liu, Tat-Seng Chua

Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner.

Stock Prediction Time Series

Structure-aware Generative Network for 3D-Shape Modeling

1 code implementation12 Aug 2018 Zhijie Wu, Xiang Wang, Di Lin, Dani Lischinski, Daniel Cohen-Or, Hui Huang

The key idea is that during the analysis, the two branches exchange information between them, thereby learning the dependencies between structure and geometry and encoding two augmented features, which are then fused into a single latent code.


Outer Product-based Neural Collaborative Filtering

1 code implementation12 Aug 2018 Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, Tat-Seng Chua

In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering.

Collaborative Filtering

Item Silk Road: Recommending Items from Information Domains to Social Users

no code implementations10 Jun 2017 Xiang Wang, Xiangnan He, Liqiang Nie, Tat-Seng Chua

In this work, we address the problem of cross-domain social recommendation, i. e., recommending relevant items of information domains to potential users of social networks.

Collaborative Ranking Recommendation Systems

Uncovering Group Level Insights with Accordant Clustering

no code implementations7 Apr 2017 Amit Dhurandhar, Margareta Ackerman, Xiang Wang

Clustering is a widely-used data mining tool, which aims to discover partitions of similar items in data.

Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection

no code implementations29 Aug 2016 Xiang Wang, Huimin Ma, Xiaozhi Chen, ShaoDi You

In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection.

RGB Salient Object Detection Saliency Detection +1

A Multilevel Coordinate Search Algorithm for Well Placement, Control and Joint Optimization

no code implementations13 Oct 2015 Xiang Wang, Ronald D. Haynes, Qihong Feng

For the joint optimization problem we compare the performance of the simultaneous and sequential procedures and show the utility of the latter.

Improving Object Proposals With Multi-Thresholding Straddling Expansion

no code implementations CVPR 2015 Xiaozhi Chen, Huimin Ma, Xiang Wang, Zhichen Zhao

Based on the characteristics of superpixel tightness distribution, we propose an effective method, namely multi-thresholding straddling expansion (MTSE) to reduce localization bias via fast diversification.

Object Detection

On Constrained Spectral Clustering and Its Applications

1 code implementation25 Jan 2012 Xiang Wang, Buyue Qian, Ian Davidson

Furthermore, by inheriting the objective function from spectral clustering and encoding the constraints explicitly, much of the existing analysis of unconstrained spectral clustering techniques remains valid for our formulation.

Transfer Learning

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