Search Results for author: Xiang Wang

Found 107 papers, 60 papers with code

Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting

1 code implementation6 Mar 2023 An Zhang, Fangfu Liu, Wenchang Ma, Zhibo Cai, Xiang Wang, Tat-Seng Chua

Despite great success in low-dimensional linear systems, it has been observed that these approaches overly exploit easier-to-fit samples, thus inevitably learning spurious edges.

Bilevel Optimization Causal Discovery

CLIP-guided Prototype Modulating for Few-shot Action Recognition

1 code implementation6 Mar 2023 Xiang Wang, Shiwei Zhang, Jun Cen, Changxin Gao, Yingya Zhang, Deli Zhao, Nong Sang

Learning from large-scale contrastive language-image pre-training like CLIP has shown remarkable success in a wide range of downstream tasks recently, but it is still under-explored on the challenging few-shot action recognition (FSAR) task.

Few-Shot action recognition Few Shot Action Recognition

Invariant Collaborative Filtering to Popularity Distribution Shift

1 code implementation10 Feb 2023 An Zhang, Jingnan Zheng, Xiang Wang, Yancheng Yuan, Tat-Seng Chua

Collaborative Filtering (CF) models, despite their great success, suffer from severe performance drops due to popularity distribution shifts, where these changes are ubiquitous and inevitable in real-world scenarios.

Collaborative Filtering Representation Learning

Causal Inference for Knowledge Graph based Recommendation

1 code implementation20 Dec 2022 Yinwei Wei, Xiang Wang, Liqiang Nie, Shaoyu Li, Dingxian Wang, Tat-Seng Chua

Knowledge Graph (KG), as a side-information, tends to be utilized to supplement the collaborative filtering (CF) based recommendation model.

Collaborative Filtering Counterfactual Inference

MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection

no code implementations25 Nov 2022 Tianpeng Bao, Jiadong Chen, Wei Li, Xiang Wang, Jingjing Fei, Liwei Wu, Rui Zhao, Ye Zheng

However, existing datasets for unsupervised anomaly detection are biased towards manufacturing inspection, not considering maintenance inspection which is usually conducted under outdoor uncontrolled environment such as varying camera viewpoints, messy background and degradation of object surface after long-term working.

Unsupervised Anomaly Detection

Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network

no code implementations19 Nov 2022 Xiang Wang, Yimin Yang, Zhichang Guo, Zhili Zhou, Yu Liu, Qixiang Pang, Shan Du

First, the UBCDTN is able to produce an approximated real-like LR image through transferring the LR image from an artificially degraded domain to the real-world LR image domain.

Image Super-Resolution Transfer Learning

Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network

no code implementations18 Nov 2022 Xiang Wang, Yimin Yang, Qixiang Pang, Xiao Lu, Yu Liu, Shan Du

In this paper, we propose a novel face super-resolution method, namely Semantic Encoder guided Generative Adversarial Face Ultra-Resolution Network (SEGA-FURN) to ultra-resolve an unaligned tiny LR face image to its HR counterpart with multiple ultra-upscaling factors (e. g., 4x and 8x).

Image Super-Resolution

Adversarial Causal Augmentation for Graph Covariate Shift

no code implementations5 Nov 2022 Yongduo Sui, Xiang Wang, Jiancan Wu, An Zhang, Xiangnan He

Existing strategies of graph invariant learning and data augmentation suffer from limited environments or unstable causal features, which greatly limits their generalization ability on covariate shift.

Data Augmentation

Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning

no code implementations2 Nov 2022 Yixuan Pei, Zhiwu Qing, Jun Cen, Xiang Wang, Shiwei Zhang, Yaxiong Wang, Mingqian Tang, Nong Sang, Xueming Qian

The former is to reduce the memory cost by preserving only one condensed frame instead of the whole video, while the latter aims to compensate the lost spatio-temporal details in the Frame Condensing stage.

Action Recognition Class Incremental Learning +1

Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup

no code implementations24 Oct 2022 Muthu Chidambaram, Xiang Wang, Chenwei Wu, Rong Ge

Mixup is a data augmentation technique that relies on training using random convex combinations of data points and their labels.

Data Augmentation Image Classification

Slippage-robust Gaze Tracking for Near-eye Display

no code implementations20 Oct 2022 Wei zhang, Jiaxi Cao, Xiang Wang, Enqi Tian, Bin Li

In recent years, head-mounted near-eye display devices have become the key hardware foundation for virtual reality and augmented reality.

Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering

1 code implementation20 Oct 2022 An Zhang, Wenchang Ma, Xiang Wang, Tat-Seng Chua

Collaborative filtering (CF) models easily suffer from popularity bias, which makes recommendation deviate from users' actual preferences.

Collaborative Filtering

Understanding Edge-of-Stability Training Dynamics with a Minimalist Example

no code implementations7 Oct 2022 Xingyu Zhu, Zixuan Wang, Xiang Wang, Mo Zhou, Rong Ge

Globally we observe that the training dynamics for our example has an interesting bifurcating behavior, which was also observed in the training of neural nets.

Vision-Based Defect Classification and Weight Estimation of Rice Kernels

no code implementations6 Oct 2022 Xiang Wang, Kai Wang, Xiaohong Li, Shiguo Lian

To compensate for the imbalance of different kernel numbers and classify kernels with multiple flaws accurately, we propose a multi-stage workflow which is able to locate the kernels in the captured image and classify their properties.

Plateau in Monotonic Linear Interpolation -- A "Biased" View of Loss Landscape for Deep Networks

no code implementations3 Oct 2022 Xiang Wang, Annie N. Wang, Mo Zhou, Rong Ge

Monotonic linear interpolation (MLI) - on the line connecting a random initialization with the minimizer it converges to, the loss and accuracy are monotonic - is a phenomenon that is commonly observed in the training of neural networks.

Equivariant and Invariant Grounding for Video Question Answering

2 code implementations26 Jul 2022 Yicong Li, Xiang Wang, Junbin Xiao, Tat-Seng Chua

Specifically, the equivariant grounding encourages the answering to be sensitive to the semantic changes in the causal scene and question; in contrast, the invariant grounding enforces the answering to be insensitive to the changes in the environment scene.

Question Answering Video Question Answering

MAR: Masked Autoencoders for Efficient Action Recognition

1 code implementation24 Jul 2022 Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Xiang Wang, Yuehuan Wang, Yiliang Lv, Changxin Gao, Nong Sang

Inspired by this, we propose propose Masked Action Recognition (MAR), which reduces the redundant computation by discarding a proportion of patches and operating only on a part of the videos.

Action Classification Action Recognition +1

Context-aware Proposal Network for Temporal Action Detection

no code implementations18 Jun 2022 Xiang Wang, Huaxin Zhang, Shiwei Zhang, Changxin Gao, Yuanjie Shao, Nong Sang

This technical report presents our first place winning solution for temporal action detection task in CVPR-2022 AcitivityNet Challenge.

Action Classification Action Detection

Let Invariant Rationale Discovery Inspire Graph Contrastive Learning

1 code implementation16 Jun 2022 Sihang Li, Xiang Wang, An Zhang, Yingxin Wu, Xiangnan He, Tat-Seng Chua

Specifically, without supervision signals, RGCL uses a rationale generator to reveal salient features about graph instance-discrimination as the rationale, and then creates rationale-aware views for contrastive learning.

Contrastive Learning

Invariant Grounding for Video Question Answering

1 code implementation CVPR 2022 Yicong Li, Xiang Wang, Junbin Xiao, Wei Ji, Tat-Seng Chua

At its core is understanding the alignments between visual scenes in video and linguistic semantics in question to yield the answer.

Question Answering Video Question Answering

CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation

1 code implementation1 Jun 2022 Yunshan Ma, Yingzhi He, An Zhang, Xiang Wang, Tat-Seng Chua

Recent methods usually take advantage of both user-bundle and user-item interactions information to obtain informative representations for users and bundles, corresponding to bundle view and item view, respectively.

Association Contrastive Learning +1

Differentiable Invariant Causal Discovery

no code implementations31 May 2022 Yu Wang, An Zhang, Xiang Wang, Yancheng Yuan, Xiangnan He, Tat-Seng Chua

This paper proposes Differentiable Invariant Causal Discovery (DICD), utilizing the multi-environment information based on a differentiable framework to avoid learning spurious edges and wrong causal directions.

Causal Discovery

Benchmarking Unsupervised Anomaly Detection and Localization

no code implementations30 May 2022 Ye Zheng, Xiang Wang, Yu Qi, Wei Li, Liwei Wu

From the time the MVTec AD dataset was proposed to the present, new research methods that are constantly being proposed push its precision to saturation.

Benchmarking Unsupervised Anomaly Detection

Copy Motion From One to Another: Fake Motion Video Generation

no code implementations3 May 2022 Zhenguang Liu, Sifan Wu, Chejian Xu, Xiang Wang, Lei Zhu, Shuang Wu, Fuli Feng

3) To enhance texture details, we encode facial features with geometric guidance and employ local GANs to refine the face, feet, and hands.

Video Generation

A Novel Speech-Driven Lip-Sync Model with CNN and LSTM

no code implementations2 May 2022 Xiaohong Li, Xiang Wang, Kai Wang, Shiguo Lian

Generating synchronized and natural lip movement with speech is one of the most important tasks in creating realistic virtual characters.

Face Model speech-recognition +1

Hybrid Relation Guided Set Matching for Few-shot Action Recognition

1 code implementation CVPR 2022 Xiang Wang, Shiwei Zhang, Zhiwu Qing, Mingqian Tang, Zhengrong Zuo, Changxin Gao, Rong Jin, Nong Sang

To overcome the two limitations, we propose a novel Hybrid Relation guided Set Matching (HyRSM) approach that incorporates two key components: hybrid relation module and set matching metric.

Few Shot Action Recognition set matching

Cross Pairwise Ranking for Unbiased Item Recommendation

1 code implementation26 Apr 2022 Qi Wan, Xiangnan He, Xiang Wang, Jiancan Wu, Wei Guo, Ruiming Tang

In this work, we develop a new learning paradigm named Cross Pairwise Ranking (CPR) that achieves unbiased recommendation without knowing the exposure mechanism.

Recommendation Systems

Reinforced Causal Explainer for Graph Neural Networks

1 code implementation23 Apr 2022 Xiang Wang, Yingxin Wu, An Zhang, Fuli Feng, Xiangnan He, Tat-Seng Chua

Such reward accounts for the dependency of the newly-added edge and the previously-added edges, thus reflecting whether they collaborate together and form a coalition to pursue better explanations.

Graph Classification

Rumor Detection with Self-supervised Learning on Texts and Social Graph

no code implementations19 Apr 2022 Yuan Gao, Xiang Wang, Xiangnan He, Huamin Feng, Yongdong Zhang

At the core is to model the rumor characteristics inherent in rich information, such as propagation patterns in social network and semantic patterns in post content, and differentiate them from the truth.

Self-Supervised Learning

Temporal Feature Alignment and Mutual Information Maximization for Video-Based Human Pose Estimation

1 code implementation CVPR 2022 Zhenguang Liu, Runyang Feng, Haoming Chen, Shuang Wu, Yixing Gao, Yunjun Gao, Xiang Wang

State-of-the-art methods strive to incorporate additional visual evidences from neighboring frames (supporting frames) to facilitate the pose estimation of the current frame (key frame).

Pose Estimation

Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective

1 code implementation CVPR 2022 Jiawei Zhang, Xiang Wang, Xiao Bai, Chen Wang, Lei Huang, Yimin Chen, Lin Gu, Jun Zhou, Tatsuya Harada, Edwin R. Hancock

The stereo contrastive feature loss function explicitly constrains the consistency between learned features of matching pixel pairs which are observations of the same 3D points.

Contrastive Learning Stereo Matching

Discovering Invariant Rationales for Graph Neural Networks

1 code implementation ICLR 2022 Ying-Xin Wu, Xiang Wang, An Zhang, Xiangnan He, Tat-Seng Chua

Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features -- rationale -- which guides the model prediction.

Graph Classification

Deconfounding to Explanation Evaluation in Graph Neural Networks

no code implementations21 Jan 2022 Ying-Xin Wu, Xiang Wang, An Zhang, Xia Hu, Fuli Feng, Xiangnan He, Tat-Seng Chua

In this work, we propose Deconfounded Subgraph Evaluation (DSE) which assesses the causal effect of an explanatory subgraph on the model prediction.

Training Free Graph Neural Networks for Graph Matching

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

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

Entity Alignment Graph Matching +1

Causal Attention for Interpretable and Generalizable Graph Classification

1 code implementation30 Dec 2021 Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, Xiangnan He, Tat-Seng Chua

To endow the classifier with better interpretation and generalization, we propose the Causal Attention Learning (CAL) strategy, which discovers the causal patterns and mitigates the confounding effect of shortcuts.

Graph Attention Graph Classification

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

3 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 #6 on Anomaly Detection on MVTec AD (using extra training data)

Unsupervised Anomaly Detection Weakly Supervised Defect 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 implementation ICLR 2022 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 #27 on Anomaly Detection on MVTec AD (using extra training data)

Contrastive Learning Unsupervised Anomaly Detection

Inductive Lottery Ticket Learning for Graph Neural Networks

no code implementations29 Sep 2021 Yongduo Sui, Xiang Wang, Tianlong Chen, Xiangnan He, Tat-Seng Chua

In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity.

Graph Classification Node Classification +1

Time-aware Path Reasoning on Knowledge Graph for Recommendation

1 code implementation5 Aug 2021 Yuyue Zhao, Xiang Wang, Jiawei Chen, Yashen Wang, Wei Tang, 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.

Explainable Recommendation 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.

Image Segmentation Semantic Segmentation +1

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.

Online Action Detection

Weakly-Supervised Temporal Action Localization Through Local-Global Background Modeling

no code implementations20 Jun 2021 Xiang Wang, Zhiwu Qing, Ziyuan Huang, Yutong Feng, Shiwei Zhang, Jianwen Jiang, Mingqian Tang, Yuanjie Shao, Nong Sang

Then our proposed Local-Global Background Modeling Network (LGBM-Net) is trained to localize instances by using only video-level labels based on Multi-Instance Learning (MIL).

Weakly-supervised Learning Weakly-supervised Temporal Action Localization +1

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 Retrieval +2

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.

Explanation Generation

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 Disentanglement

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 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

11 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.

BIG-bench Machine Learning

Neural Graph Collaborative Filtering

16 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

7 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.

Explainable Recommendation Knowledge Graphs +1

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

3 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 Analysis

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

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.


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.

object-detection RGB Salient Object Detection +2

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 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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