Search Results for author: Xiang Wang

Found 162 papers, 105 papers with code

Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model

1 code implementation28 Mar 2024 Zhicai Wang, Longhui Wei, Tan Wang, Heyu Chen, Yanbin Hao, Xiang Wang, Xiangnan He, Qi Tian

Text-to-image (T2I) generative models have recently emerged as a powerful tool, enabling the creation of photo-realistic images and giving rise to a multitude of applications.

Data Augmentation Image Classification

SELECTOR: Heterogeneous graph network with convolutional masked autoencoder for multimodal robust prediction of cancer survival

1 code implementation14 Mar 2024 Liangrui Pan, Yijun Peng, Yan Li, Xiang Wang, Wenjuan Liu, Liwen Xu, Qingchun Liang, Shaoliang Peng

To mitigate the impact of missing features within the modality on prediction accuracy, we devised a convolutional masked autoencoder (CMAE) to process the heterogeneous graph post-feature reconstruction.

Survival Prediction

PET-SQL: A Prompt-enhanced Two-stage Text-to-SQL Framework with Cross-consistency

1 code implementation13 Mar 2024 Zhishuai Li, Xiang Wang, Jingjing Zhao, Sun Yang, Guoqing Du, Xiaoru Hu, Bin Zhang, Yuxiao Ye, Ziyue Li, Rui Zhao, Hangyu Mao

Then, in the first stage, question-SQL pairs are retrieved as few-shot demonstrations, prompting the LLM to generate a preliminary SQL (PreSQL).

In-Context Learning Text-To-SQL

GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detection

1 code implementation10 Mar 2024 Huaxin Zhang, Xiang Wang, Xiaohao Xu, Xiaonan Huang, Chuchu Han, Yuehuan Wang, Changxin Gao, Shanjun Zhang, Nong Sang

In recent years, video anomaly detection has been extensively investigated in both unsupervised and weakly supervised settings to alleviate costly temporal labeling.

Anomaly Detection Video Anomaly Detection

Conformal Shield: A Novel Adversarial Attack Detection Framework for Automatic Modulation Classification

no code implementations27 Feb 2024 Tailai Wen, Da Ke, Xiang Wang, Zhitao Huang

Deep learning algorithms have become an essential component in the field of cognitive radio, especially playing a pivotal role in automatic modulation classification.

Adversarial Attack Detection Classification +1

General Debiasing for Graph-based Collaborative Filtering via Adversarial Graph Dropout

1 code implementation21 Feb 2024 An Zhang, Wenchang Ma, Pengbo Wei, Leheng Sheng, Xiang Wang

However, we have discovered that this aggregation mechanism comes with a drawback, which amplifies biases present in the interaction graph.

Collaborative Filtering Recommendation Systems +1

MolTC: Towards Molecular Relational Modeling In Language Models

1 code implementation6 Feb 2024 Junfeng Fang, Shuai Zhang, Chang Wu, Zhengyi Yang, Zhiyuan Liu, Sihang Li, Kun Wang, Wenjie Du, Xiang Wang

Molecular Relational Learning (MRL), aiming to understand interactions between molecular pairs, plays a pivotal role in advancing biochemical research.

Relational Reasoning

DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation

no code implementations5 Feb 2024 Yuan Gao, Haokun Chen, Xiang Wang, Zhicai Wang, Xue Wang, Jinyang Gao, Bolin Ding

Our research demonstrates the efficacy of leveraging AIGS and the DiffsFormer architecture to mitigate data scarcity in stock forecasting tasks.

Alleviating Structural Distribution Shift in Graph Anomaly Detection

1 code implementation25 Jan 2024 Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, Yongdong Zhang

Graph anomaly detection (GAD) is a challenging binary classification problem due to its different structural distribution between anomalies and normal nodes -- abnormal nodes are a minority, therefore holding high heterophily and low homophily compared to normal nodes.

Binary Classification Graph Anomaly Detection

Towards 3D Molecule-Text Interpretation in Language Models

1 code implementation25 Jan 2024 Sihang Li, Zhiyuan Liu, Yanchen Luo, Xiang Wang, Xiangnan He, Kenji Kawaguchi, Tat-Seng Chua, Qi Tian

Through 3D molecule-text alignment and 3D molecule-centric instruction tuning, 3D-MoLM establishes an integration of 3D molecular encoder and LM.

Instruction Following Language Modelling +3

Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security

2 code implementations10 Jan 2024 Yuanchun Li, Hao Wen, Weijun Wang, Xiangyu Li, Yizhen Yuan, Guohong Liu, Jiacheng Liu, Wenxing Xu, Xiang Wang, Yi Sun, Rui Kong, Yile Wang, Hanfei Geng, Jian Luan, Xuefeng Jin, Zilong Ye, Guanjing Xiong, Fan Zhang, Xiang Li, Mengwei Xu, Zhijun Li, Peng Li, Yang Liu, Ya-Qin Zhang, Yunxin Liu

Next, we discuss several key challenges to achieve intelligent, efficient and secure Personal LLM Agents, followed by a comprehensive survey of representative solutions to address these challenges.

A Recipe for Scaling up Text-to-Video Generation with Text-free Videos

1 code implementation25 Dec 2023 Xiang Wang, Shiwei Zhang, Hangjie Yuan, Zhiwu Qing, Biao Gong, Yingya Zhang, Yujun Shen, Changxin Gao, Nong Sang

Following such a pipeline, we study the effect of doubling the scale of training set (i. e., video-only WebVid10M) with some randomly collected text-free videos and are encouraged to observe the performance improvement (FID from 9. 67 to 8. 19 and FVD from 484 to 441), demonstrating the scalability of our approach.

Text-to-Image Generation Text-to-Video Generation +2

BSL: Understanding and Improving Softmax Loss for Recommendation

1 code implementation20 Dec 2023 Junkang Wu, Jiawei Chen, Jiancan Wu, Wentao Shi, Jizhi Zhang, Xiang Wang

Loss functions steer the optimization direction of recommendation models and are critical to model performance, but have received relatively little attention in recent recommendation research.


InstructVideo: Instructing Video Diffusion Models with Human Feedback

1 code implementation19 Dec 2023 Hangjie Yuan, Shiwei Zhang, Xiang Wang, Yujie Wei, Tao Feng, Yining Pan, Yingya Zhang, Ziwei Liu, Samuel Albanie, Dong Ni

To tackle this problem, we propose InstructVideo to instruct text-to-video diffusion models with human feedback by reward fine-tuning.

Video Generation

DreamTalk: When Expressive Talking Head Generation Meets Diffusion Probabilistic Models

no code implementations15 Dec 2023 Yifeng Ma, Shiwei Zhang, Jiayu Wang, Xiang Wang, Yingya Zhang, Zhidong Deng

In this work, we propose a DreamTalk framework to fulfill this gap, which employs meticulous design to unlock the potential of diffusion models in generating expressive talking heads.

Denoising Talking Head Generation

VideoLCM: Video Latent Consistency Model

2 code implementations14 Dec 2023 Xiang Wang, Shiwei Zhang, Han Zhang, Yu Liu, Yingya Zhang, Changxin Gao, Nong Sang

Consistency models have demonstrated powerful capability in efficient image generation and allowed synthesis within a few sampling steps, alleviating the high computational cost in diffusion models.

Computational Efficiency Image Generation +1

Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation

1 code implementation7 Dec 2023 Zhiwu Qing, Shiwei Zhang, Jiayu Wang, Xiang Wang, Yujie Wei, Yingya Zhang, Changxin Gao, Nong Sang

At the structure level, we decompose the T2V task into two steps, including spatial reasoning and temporal reasoning, using a unified denoiser.

Text-to-Video Generation Video Generation

LLaRA: Large Language-Recommendation Assistant

1 code implementation5 Dec 2023 Jiayi Liao, Sihang Li, Zhengyi Yang, Jiancan Wu, Yancheng Yuan, Xiang Wang, Xiangnan He

Treating the "sequential behaviors of users" as a distinct modality beyond texts, we employ a projector to align the traditional recommender's ID embeddings with the LLM's input space.

Language Modelling Sequential Recommendation +1

SCTc-TE: A Comprehensive Formulation and Benchmark for Temporal Event Forecasting

1 code implementation2 Dec 2023 Yunshan Ma, Chenchen Ye, Zijian Wu, Xiang Wang, Yixin Cao, Liang Pang, Tat-Seng Chua

Temporal complex event forecasting aims to predict the future events given the observed events from history.

MultiCBR: Multi-view Contrastive Learning for Bundle Recommendation

1 code implementation28 Nov 2023 Yunshan Ma, Yingzhi He, Xiang Wang, Yinwei Wei, Xiaoyu Du, Yuyangzi Fu, Tat-Seng Chua

It does, however, have two limitations: 1) the two-view formulation does not fully exploit all the heterogeneous relations among users, bundles and items; and 2) the "early contrast and late fusion" framework is less effective in capturing user preference and difficult to generalize to multiple views.

Contrastive Learning Representation Learning

I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models

3 code implementations7 Nov 2023 Shiwei Zhang, Jiayu Wang, Yingya Zhang, Kang Zhao, Hangjie Yuan, Zhiwu Qin, Xiang Wang, Deli Zhao, Jingren Zhou

By this means, I2VGen-XL can simultaneously enhance the semantic accuracy, continuity of details and clarity of generated videos.

Large Language Model Can Interpret Latent Space of Sequential Recommender

2 code implementations31 Oct 2023 Zhengyi Yang, Jiancan Wu, Yanchen Luo, Jizhi Zhang, Yancheng Yuan, An Zhang, Xiang Wang, Xiangnan He

Sequential recommendation is to predict the next item of interest for a user, based on her/his interaction history with previous items.

Language Modelling Large Language Model +1

Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion

1 code implementation NeurIPS 2023 Zhengyi Yang, Jiancan Wu, Zhicai Wang, Xiang Wang, Yancheng Yuan, Xiangnan He

Scrutinizing previous studies, we can summarize a common learning-to-classify paradigm -- given a positive item, a recommender model performs negative sampling to add negative items and learns to classify whether the user prefers them or not, based on his/her historical interaction sequence.

Denoising Sequential Recommendation

Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss

1 code implementation NeurIPS 2023 An Zhang, Leheng Sheng, Zhibo Cai, Xiang Wang, Tat-Seng Chua

To bridge the gap, we delve into the reasons underpinning the success of contrastive loss in CF, and propose a principled Adversarial InfoNCE loss (AdvInfoNCE), which is a variant of InfoNCE, specially tailored for CF methods.

Collaborative Filtering Contrastive Learning +3

Leveraging Multimodal Features and Item-level User Feedback for Bundle Construction

1 code implementation28 Oct 2023 Yunshan Ma, Xiaohao Liu, Yinwei Wei, Zhulin Tao, Xiang Wang, Tat-Seng Chua

Specifically, we use self-attention modules to combine the multimodal and multi-item features, and then leverage both item- and bundle-level contrastive learning to enhance the representation learning, thus to counter the modality missing, noise, and sparsity problems.

Contrastive Learning Representation Learning

Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation

no code implementations25 Oct 2023 Chengpeng Li, Zhengyi Yang, Jizhi Zhang, Jiancan Wu, Dingxian Wang, Xiangnan He, Xiang Wang

Therefore, the data sparsity issue of reward signals and state transitions is very severe, while it has long been overlooked by existing RL recommenders. Worse still, RL methods learn through the trial-and-error mode, but negative feedback cannot be obtained in implicit feedback recommendation tasks, which aggravates the overestimation problem of offline RL recommender.

Contrastive Learning Offline RL +3

Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules

1 code implementation NeurIPS 2023 Zhiyuan Liu, Yaorui Shi, An Zhang, Enzhi Zhang, Kenji Kawaguchi, Xiang Wang, Tat-Seng Chua

Our results show that a subgraph-level tokenizer and a sufficiently expressive decoder with remask decoding have a large impact on the encoder's representation learning.

Representation Learning Self-Supervised Learning

ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction

1 code implementation20 Oct 2023 Yaorui Shi, An Zhang, Enzhi Zhang, Zhiyuan Liu, Xiang Wang

Predicting chemical reactions, a fundamental challenge in chemistry, involves forecasting the resulting products from a given reaction process.

Chemical Reaction Prediction

A Comprehensive Evaluation of Large Language Models on Legal Judgment Prediction

1 code implementation18 Oct 2023 Ruihao Shui, Yixin Cao, Xiang Wang, Tat-Seng Chua

Large language models (LLMs) have demonstrated great potential for domain-specific applications, such as the law domain.

Information Retrieval Legal Reasoning +1

On Generative Agents in Recommendation

1 code implementation16 Oct 2023 An Zhang, Leheng Sheng, Yuxin Chen, Hao Li, Yang Deng, Xiang Wang, Tat-Seng Chua

Recommender systems are the cornerstone of today's information dissemination, yet a disconnect between offline metrics and online performance greatly hinders their development.

Collaborative Filtering Movie Recommendation +1

Robust Collaborative Filtering to Popularity Distribution Shift

1 code implementation16 Oct 2023 An Zhang, Wenchang Ma, Jingnan Zheng, Xiang Wang, Tat-Seng Chua

The popularity shortcut tricks are good for in-distribution (ID) performance but poorly generalized to out-of-distribution (OOD) data, i. e., when popularity distribution of test data shifts w. r. t.

Collaborative Filtering

Few-shot Action Recognition with Captioning Foundation Models

no code implementations16 Oct 2023 Xiang Wang, Shiwei Zhang, Hangjie Yuan, Yingya Zhang, Changxin Gao, Deli Zhao, Nong Sang

In this paper, we develop an effective plug-and-play framework called CapFSAR to exploit the knowledge of multimodal models without manually annotating text.

Few-Shot action recognition Few Shot Action Recognition

Query and Response Augmentation Cannot Help Out-of-domain Math Reasoning Generalization

1 code implementation9 Oct 2023 Chengpeng Li, Zheng Yuan, Hongyi Yuan, Guanting Dong, Keming Lu, Jiancan Wu, Chuanqi Tan, Xiang Wang, Chang Zhou

In this paper, we conduct an investigation for such data augmentation in math reasoning and are intended to answer: (1) What strategies of data augmentation are more effective; (2) What is the scaling relationship between the amount of augmented data and model performance; and (3) Can data augmentation incentivize generalization to out-of-domain mathematical reasoning tasks?

Ranked #50 on Math Word Problem Solving on MATH (using extra training data)

Arithmetic Reasoning Data Augmentation +3

Text-to-Image Generation for Abstract Concepts

no code implementations26 Sep 2023 Jiayi Liao, Xu Chen, Qiang Fu, Lun Du, Xiangnan He, Xiang Wang, Shi Han, Dongmei Zhang

Recent years have witnessed the substantial progress of large-scale models across various domains, such as natural language processing and computer vision, facilitating the expression of concrete concepts.

Text-to-Image Generation

RLIPv2: Fast Scaling of Relational Language-Image Pre-training

3 code implementations ICCV 2023 Hangjie Yuan, Shiwei Zhang, Xiang Wang, Samuel Albanie, Yining Pan, Tao Feng, Jianwen Jiang, Dong Ni, Yingya Zhang, Deli Zhao

In this paper, we propose RLIPv2, a fast converging model that enables the scaling of relational pre-training to large-scale pseudo-labelled scene graph data.

 Ranked #1 on Zero-Shot Human-Object Interaction Detection on HICO-DET (using extra training data)

Graph Generation Human-Object Interaction Detection +6

ModelScope Text-to-Video Technical Report

3 code implementations12 Aug 2023 Jiuniu Wang, Hangjie Yuan, Dayou Chen, Yingya Zhang, Xiang Wang, Shiwei Zhang

This paper introduces ModelScopeT2V, a text-to-video synthesis model that evolves from a text-to-image synthesis model (i. e., Stable Diffusion).

Denoising Image Generation +1

Context-aware Event Forecasting via Graph Disentanglement

1 code implementation12 Aug 2023 Yunshan Ma, Chenchen Ye, Zijian Wu, Xiang Wang, Yixin Cao, Tat-Seng Chua

The task of event forecasting aims to model the relational and temporal patterns based on historical events and makes forecasting to what will happen in the future.

Disentanglement Link Prediction

Online Distillation-enhanced Multi-modal Transformer for Sequential Recommendation

1 code implementation8 Aug 2023 Wei Ji, Xiangyan Liu, An Zhang, Yinwei Wei, Yongxin Ni, Xiang Wang

To be specific, we first introduce an ID-aware Multi-modal Transformer module in the item representation learning stage to facilitate information interaction among different features.

Collaborative Filtering Representation Learning +1

Adaptive Semantic Consistency for Cross-domain Few-shot Classification

no code implementations1 Aug 2023 Hengchu Lu, Yuanjie Shao, Xiang Wang, Changxin Gao

In this way, the proposed ASC enables explicit transfer of source domain knowledge to prevent the model from overfitting the target domain.

Classification Cross-Domain Few-Shot

Discovering Dynamic Causal Space for DAG Structure Learning

1 code implementation5 Jun 2023 Fangfu Liu, Wenchang Ma, An Zhang, Xiang Wang, Yueqi Duan, Tat-Seng Chua

Discovering causal structure from purely observational data (i. e., causal discovery), aiming to identify causal relationships among variables, is a fundamental task in machine learning.

Causal Discovery Combinatorial Optimization

GIF: A General Graph Unlearning Strategy via Influence Function

1 code implementation6 Apr 2023 Jiancan Wu, Yi Yang, Yuchun Qian, Yongduo Sui, Xiang Wang, Xiangnan He

Then, we recognize the crux to the inability of traditional influence function for graph unlearning, and devise Graph Influence Function (GIF), a model-agnostic unlearning method that can efficiently and accurately estimate parameter changes in response to a $\epsilon$-mass perturbation in deleted data.

Machine Unlearning

MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action Recognition

1 code implementation CVPR 2023 Xiang Wang, Shiwei Zhang, Zhiwu Qing, Changxin Gao, Yingya Zhang, Deli Zhao, Nong Sang

To address these issues, we develop a Motion-augmented Long-short Contrastive Learning (MoLo) method that contains two crucial components, including a long-short contrastive objective and a motion autodecoder.

Contrastive Learning Few-Shot action recognition +1

Enlarging Instance-specific and Class-specific Information for Open-set Action Recognition

1 code implementation CVPR 2023 Jun Cen, Shiwei Zhang, Xiang Wang, Yixuan Pei, Zhiwu Qing, Yingya Zhang, Qifeng Chen

In this paper, we begin with analyzing the feature representation behavior in the open-set action recognition (OSAR) problem based on the information bottleneck (IB) theory, and propose to enlarge the instance-specific (IS) and class-specific (CS) information contained in the feature for better performance.

Open Set Action Recognition

Multi-task Adversarial Learning for Semi-supervised Trajectory-User Linking

1 code implementation ECML-PKDD 2023 Sen Zhang, Senzhang Wang, Xiang Wang, Shigeng Zhang, Hao Miao & Junxing Zhu

We first project users and trajectories into the common latent feature space through learning a projection function (generator) to minimize the distance between the user distribution and the trajectory distribution.

Multi-Task Learning

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

Space-time Prompting for Video Class-incremental Learning

no code implementations ICCV 2023 Yixuan Pei, Zhiwu Qing, Shiwei Zhang, Xiang Wang, Yingya Zhang, Deli Zhao, Xueming Qian

In this paper, we will fill this gap by learning multiple prompts based on a powerful image-language pre-trained model, i. e., CLIP, making it fit for video class-incremental learning (VCIL).

Class Incremental Learning Incremental 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 +1

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.

Generative Adversarial Network Image Super-Resolution +1

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

Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift

1 code implementation NeurIPS 2023 Yongduo Sui, Qitian Wu, Jiancan Wu, Qing Cui, Longfei Li, Jun Zhou, Xiang Wang, Xiangnan He

From the perspective of invariant learning and stable learning, a recently well-established paradigm for out-of-distribution generalization, stable features of the graph are assumed to causally determine labels, while environmental features tend to be unstable and can lead to the two primary types of distribution shifts.

Data Augmentation Graph Classification +2

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

1 code implementation24 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

1 code implementation26 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.

Contrastive Learning Graph Learning

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 Relation +1

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

5 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.

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

2 code implementations11 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

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.

Foreground Segmentation Image Segmentation +3

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

Relation Modeling in Spatio-Temporal Action Localization

no code implementations15 Jun 2021 Yutong Feng, Jianwen Jiang, Ziyuan Huang, Zhiwu Qing, Xiang Wang, Shiwei Zhang, Mingqian Tang, Yue Gao

This paper presents our solution to the AVA-Kinetics Crossover Challenge of ActivityNet workshop at CVPR 2021.

Ranked #4 on Spatio-Temporal Action Localization on AVA-Kinetics (using extra training data)

Action Detection Relation +2

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

2 code implementations14 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 Relation

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

2 code implementations21 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.

Attribute 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.

Computational Efficiency 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.

Segmentation Weakly supervised Semantic Segmentation +1

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

16 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.


MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video

1 code implementation ACM International Conference on Multimedia 2019 Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, Tat-Seng Chua

Existing works on multimedia recommendation largely exploit multi-modal contents to enrich item representations, while less effort is made to leverage information interchange between users and items to enhance user representations and further capture user's fine-grained preferences on different modalities.

Microvideo Recommendation Micro-video recommendations +4

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

19 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.

Segmentation 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