Search Results for author: Meng Liu

Found 70 papers, 37 papers with code

Enhancing Visible-Infrared Person Re-identification with Modality- and Instance-aware Visual Prompt Learning

no code implementations18 Jun 2024 Ruiqi Wu, Bingliang Jiao, Wenxuan Wang, Meng Liu, Peng Wang

In this model, we have designed a series of modality-specific prompts, which could enable our model to adapt to and make use of the specific information inherent in different modality inputs, thereby reducing the interference caused by the modality gap and achieving better identification.

Person Re-Identification

An Empirical Study on the Fairness of Foundation Models for Multi-Organ Image Segmentation

no code implementations18 Jun 2024 Qin Li, Yizhe Zhang, Yan Li, Jun Lyu, Meng Liu, Longyu Sun, Mengting Sun, Qirong Li, Wenyue Mao, Xinran Wu, Yajing Zhang, Yinghua Chu, Shuo Wang, Chengyan Wang

We test state-of-the-art foundation models for medical image segmentation, including the original SAM, medical SAM and SAT models, to evaluate segmentation efficacy across different demographic groups and identify disparities.

Fairness Image Segmentation +3

DualBind: A Dual-Loss Framework for Protein-Ligand Binding Affinity Prediction

no code implementations11 Jun 2024 Meng Liu, Saee Gopal Paliwal

Accurate prediction of protein-ligand binding affinities is crucial for drug development.


Async Learned User Embeddings for Ads Delivery Optimization

no code implementations9 Jun 2024 Mingwei Tang, Meng Liu, Hong Li, Junjie Yang, Chenglin Wei, Boyang Li, Dai Li, Rengan Xu, Yifan Xu, Zehua Zhang, Xiangyu Wang, Linfeng Liu, Yuelei Xie, Chengye Liu, Labib Fawaz, Li Li, Hongnan Wang, Bill Zhu, Sri Reddy

User representation is crucial for recommendation systems as it helps to deliver personalized recommendations by capturing user preferences and behaviors in low-dimensional vectors.

Graph Learning Recommendation Systems +1

Breaking Through the Noisy Correspondence: A Robust Model for Image-Text Matching

no code implementations ACM Transactions on Information Systems 2024 Haitao Shi, Meng Liu, Xiaoxuan Mu, Xuemeng Song, Yupeng Hu, Liqiang Nie

To reduce the negative impact of noisy correspondence, we propose a novel model that first transforms the noisy correspondence filtering problem into a similarity distribution modeling problem by exploiting the powerful capabilities of pre-trained models.

Cross-modal retrieval with noisy correspondence Image-text matching +1

Research on geometric figure classification algorithm based on Deep Learning

no code implementations25 Apr 2024 Ruiyang Wang, Haonan Wang, Junfeng Sun, Mingjia Zhao, Meng Liu

In recent years, with the rapid development of computer information technology, the development of artificial intelligence has been accelerating.

Learning Theory

On the Markov Property of Neural Algorithmic Reasoning: Analyses and Methods

no code implementations7 Mar 2024 Montgomery Bohde, Meng Liu, Alexandra Saxton, Shuiwang Ji

To address challenges in training ForgetNet at early stages, we further introduce G-ForgetNet, which uses a gating mechanism to allow for the selective integration of historical embeddings.

Sentiment-enhanced Graph-based Sarcasm Explanation in Dialogue

no code implementations6 Feb 2024 Kun Ouyang, Liqiang Jing, Xuemeng Song, Meng Liu, Yupeng Hu, Liqiang Nie

Although existing studies have achieved great success based on the generative pretrained language model BART, they overlook exploiting the sentiments residing in the utterance, video and audio, which are vital clues for sarcasm explanation.

Explanation Generation Language Modelling +1

SPT: Spectral Transformer for Red Giant Stars Age and Mass Estimation

no code implementations10 Jan 2024 Mengmeng Zhang, Fan Wu, Yude Bu, Shanshan Li, Zhenping Yi, Meng Liu, Xiaoming Kong

The age and mass of red giants are essential for understanding the structure and evolution of the Milky Way.


Uncovering Hidden Connections: Iterative Search and Reasoning for Video-grounded Dialog

1 code implementation11 Oct 2023 Haoyu Zhang, Meng Liu, YaoWei Wang, Da Cao, Weili Guan, Liqiang Nie

In response to these challenges, we present an iterative search and reasoning framework, which consists of a textual encoder, a visual encoder, and a generator.

Question Answering Response Generation +1

TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification

1 code implementation21 Sep 2023 Meng Liu, Ke Liang, Dayu Hu, Hao Yu, Yue Liu, Lingyuan Meng, Wenxuan Tu, Sihang Zhou, Xinwang Liu

We observe that these audiovisual data naturally have temporal attributes, such as the time information for each frame in the video.

Graph Learning

UniSA: Unified Generative Framework for Sentiment Analysis

2 code implementations4 Sep 2023 Zaijing Li, Ting-En Lin, Yuchuan Wu, Meng Liu, Fengxiao Tang, Ming Zhao, Yongbin Li

Sentiment analysis is a crucial task that aims to understand people's emotional states and predict emotional categories based on multimodal information.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2

Reinforcement Graph Clustering with Unknown Cluster Number

2 code implementations13 Aug 2023 Yue Liu, Ke Liang, Jun Xia, Xihong Yang, Sihang Zhou, Meng Liu, Xinwang Liu, Stan Z. Li

To enable the deep graph clustering algorithms to work without the guidance of the predefined cluster number, we propose a new deep graph clustering method termed Reinforcement Graph Clustering (RGC).

Clustering Graph Clustering +1

Structure Guided Multi-modal Pre-trained Transformer for Knowledge Graph Reasoning

no code implementations6 Jul 2023 Ke Liang, Sihang Zhou, Yue Liu, Lingyuan Meng, Meng Liu, Xinwang Liu

To this end, we propose the graph Structure Guided Multimodal Pretrained Transformer for knowledge graph reasoning, termed SGMPT.

Knowledge Graphs Question Answering +2

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules

1 code implementation NeurIPS 2023 Haiyang Yu, Meng Liu, Youzhi Luo, Alex Strasser, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji

Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT).

Atomic Forces

A Survey on Video Moment Localization

no code implementations13 Jun 2023 Meng Liu, Liqiang Nie, Yunxiao Wang, Meng Wang, Yong Rui

Video moment localization, also known as video moment retrieval, aiming to search a target segment within a video described by a given natural language query.

Moment Retrieval Retrieval +1

Graph Mixup with Soft Alignments

1 code implementation11 Jun 2023 Hongyi Ling, Zhimeng Jiang, Meng Liu, Shuiwang Ji, Na Zou

We conduct systematic experiments to show that S-Mixup can improve the performance and generalization of graph neural networks (GNNs) on various graph classification tasks.

Data Augmentation Graph Classification

G$^2$uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering

no code implementations8 Jun 2023 Hao Yu, Chuan Ma, Meng Liu, Tianyu Du, Ming Ding, Tao Xiang, Shouling Ji, Xinwang Liu

Through empirical evaluation, comparing G$^2$uardFL with cutting-edge defenses, such as FLAME (USENIX Security 2022) [28] and DeepSight (NDSS 2022) [36], against various backdoor attacks including 3DFed (SP 2023) [20], our results demonstrate its significant effectiveness in mitigating backdoor attacks while having a negligible impact on the aggregated model's performance on benign samples (i. e., the primary task performance).

Anomaly Detection Clustering +2

Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization

2 code implementations NeurIPS 2023 Shurui Gui, Meng Liu, Xiner Li, Youzhi Luo, Shuiwang Ji

In this work, we propose to simultaneously incorporate label and environment causal independence (LECI) to fully make use of label and environment information, thereby addressing the challenges faced by prior methods on identifying causal and invariant subgraphs.

Out-of-Distribution Generalization

Message Intercommunication for Inductive Relation Reasoning

no code implementations23 May 2023 Ke Liang, Lingyuan Meng, Sihang Zhou, Siwei Wang, Wenxuan Tu, Yue Liu, Meng Liu, Xinwang Liu

However, the uni-directional message-passing mechanism hinders such models from exploiting hidden mutual relations between entities in directed graphs.

Knowledge Graphs Relation

Deep Temporal Graph Clustering

1 code implementation18 May 2023 Meng Liu, Yue Liu, Ke Liang, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu

To solve the problem, we propose a general framework for deep Temporal Graph Clustering called TGC, which introduces deep clustering techniques to suit the interaction sequence-based batch-processing pattern of temporal graphs.

Clustering Deep Clustering +4

Learnable Pillar-based Re-ranking for Image-Text Retrieval

1 code implementation25 Apr 2023 Leigang Qu, Meng Liu, Wenjie Wang, Zhedong Zheng, Liqiang Nie, Tat-Seng Chua

Image-text retrieval aims to bridge the modality gap and retrieve cross-modal content based on semantic similarities.

Re-Ranking Retrieval +1

SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoning

no code implementations20 Apr 2023 Lingyuan Meng, Ke Liang, Bin Xiao, Sihang Zhou, Yue Liu, Meng Liu, Xihong Yang, Xinwang Liu

Moreover, most of the existing methods ignore leveraging the beneficial information from aliasing relations (AR), i. e., data-rich relations with similar contextual semantics to the target data-poor relation.

Knowledge Graphs Relation +1

Cross-modal Audio-visual Co-learning for Text-independent Speaker Verification

1 code implementation22 Feb 2023 Meng Liu, Kong Aik Lee, Longbiao Wang, Hanyi Zhang, Chang Zeng, Jianwu Dang

Visual speech (i. e., lip motion) is highly related to auditory speech due to the co-occurrence and synchronization in speech production.

Text-Independent Speaker Verification

Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment

no code implementations15 Feb 2023 Meng Liu, Ke Liang, Yawei Zhao, Wenxuan Tu, Sihang Zhou, Xinbiao Gan, Xinwang Liu, Kunlun He

To address this issue, we propose a self-supervised method called S2T for temporal graph learning, which extracts both temporal and structural information to learn more informative node representations.

Graph Learning

A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal

1 code implementation12 Dec 2022 Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu, Fuchun Sun

According to the graph types, existing KGR models can be roughly divided into three categories, i. e., static models, temporal models, and multi-modal models.

General Knowledge Knowledge Graph Embedding +3

COVID-19 Activity Risk Calculator as a Gamified Public Health Intervention Tool

1 code implementation5 Dec 2022 Shreyasvi Natraj, Malhar Bhide, Nathan Yap, Meng Liu, Agrima Seth, Jonathan Berman, Christin Glorioso

In order to create a simple easy-to-use tool for estimating different individual risks associated with carrying out daily-life activity, we developed COVID-19 Activity Risk Calculator (CovARC).

Coordinating Cross-modal Distillation for Molecular Property Prediction

no code implementations30 Nov 2022 Hao Zhang, Nan Zhang, Ruixin Zhang, Lei Shen, Yingyi Zhang, Meng Liu

The existing graph methods have demonstrated that 3D geometric information is significant for better performance in MPP.

Graph Regression Graph Representation Learning +4

DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding

no code implementations21 Nov 2022 Haitao Lin, Yufei Huang, Meng Liu, Xuanjing Li, Shuiwang Ji, Stan Z. Li

Previous works usually generate atoms in an auto-regressive way, where element types and 3D coordinates of atoms are generated one by one.

Drug Discovery

FedVMR: A New Federated Learning method for Video Moment Retrieval

no code implementations28 Oct 2022 Yan Wang, Xin Luo, Zhen-Duo Chen, Peng-Fei Zhang, Meng Liu, Xin-Shun Xu

As the first that is explored in VMR field, the new task is defined as video moment retrieval with distributed data.

Federated Learning Moment Retrieval +1

Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models

1 code implementation11 Oct 2022 Meng Liu, Haoran Liu, Shuiwang Ji

the discrete data space to approximately construct the provably optimal proposal distribution, which is subsequently used by importance sampling to efficiently estimate the original ratio matching objective.

Graph Generation

Spoofing-Aware Attention based ASV Back-end with Multiple Enrollment Utterances and a Sampling Strategy for the SASV Challenge 2022

no code implementations1 Sep 2022 Chang Zeng, Lin Zhang, Meng Liu, Junichi Yamagishi

Current state-of-the-art automatic speaker verification (ASV) systems are vulnerable to presentation attacks, and several countermeasures (CMs), which distinguish bona fide trials from spoofing ones, have been explored to protect ASV.

Speaker Verification

Topological structure of complex predictions

1 code implementation28 Jul 2022 Meng Liu, Tamal K. Dey, David F. Gleich

Complex prediction models such as deep learning are the output from fitting machine learning, neural networks, or AI models to a set of training data.

Image Classification Topological Data Analysis

GraphFM: Improving Large-Scale GNN Training via Feature Momentum

1 code implementation14 Jun 2022 Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, Shuiwang Ji

GraphFM-IB applies FM to in-batch sampled data, while GraphFM-OB applies FM to out-of-batch data that are 1-hop neighborhood of in-batch data.

Node Classification

Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm

no code implementations4 Jun 2022 Meng Liu, Haiyang Yu, Shuiwang Ji

Message passing graph neural networks (GNNs) are known to have their expressiveness upper-bounded by 1-dimensional Weisfeiler-Leman (1-WL) algorithm.

Generating 3D Molecules for Target Protein Binding

1 code implementation19 Apr 2022 Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, Shuiwang Ji

Second, to preserve the desirable equivariance property, we select a local reference atom according to the designed auxiliary classifiers and then construct a local spherical coordinate system.

Drug Discovery Graph Neural Network

Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences

1 code implementation7 Feb 2022 Meng Liu, Shuiwang Ji

Therefore, our Neighbor2Seq naturally endows GNNs with the efficiency and advantages of deep learning operations on grid-like data by precomputing the Neighbor2Seq transformations.

Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community Influences

1 code implementation1 Oct 2021 Meng Liu, Yong liu

Therefore, we propose a new inductive network representation learning method called MNCI by mining neighborhood and community influences in temporal networks.

Link Prediction Node Classification +1

Gradient-Guided Importance Sampling for Learning Discrete Energy-Based Models

1 code implementation29 Sep 2021 Meng Liu, Haoran Liu, Shuiwang Ji

In this study, we propose ratio matching with gradient-guided importance sampling (RMwGGIS) to alleviate the above limitations.

Graph Generation

GraphEBM: Towards Permutation Invariant and Multi-Objective Molecular Graph Generation

no code implementations29 Sep 2021 Meng Liu, Keqiang Yan, Bora Oztekin, Shuiwang Ji

In this work, we propose GraphEBM, a molecular graph generation method via energy-based models (EBMs), as an exploratory work to perform permutation invariant and multi-objective molecule generation.

Drug Discovery Graph Generation +1

A Novel Patch Convolutional Neural Network for View-based 3D Model Retrieval

no code implementations25 Sep 2021 Zan Gao, Yuxiang Shao, Weili Guan, Meng Liu, Zhiyong Cheng, ShengYong Chen

Thus, we tackle this problem from the perspective of exploiting the relationships between patch features to capture long-range associations among multi-view images.


Multigranular Visual-Semantic Embedding for Cloth-Changing Person Re-identification

no code implementations10 Aug 2021 Zan Gao, Hongwei Wei, Weili Guan, Weizhi Nie, Meng Liu, Meng Wang

To solve these issues, in this work, a novel multigranular visual-semantic embedding algorithm (MVSE) is proposed for cloth-changing person ReID, where visual semantic information and human attributes are embedded into the network, and the generalized features of human appearance can be well learned to effectively solve the problem of clothing changes.

Cloth-Changing Person Re-Identification

Dynamic Modality Interaction Modeling for Image-Text Retrieval

1 code implementation ACM Special Interest Group on Information Retrieval 2021 Leigang Qu, Meng Liu, Jianlong Wu, Zan Gao, Liqiang Nie

To address these issues, we develop a novel modality interaction modeling network based upon the routing mechanism, which is the first unified and dynamic multimodal interaction framework towards image-text retrieval.

Cross-Modal Retrieval Information Retrieval +2

Multi-Modal Relational Graph for Cross-Modal Video Moment Retrieval

no code implementations CVPR 2021 Yawen Zeng, Da Cao, Xiaochi Wei, Meng Liu, Zhou Zhao, Zheng Qin

Toward this end, we contribute a multi-modal relational graph to capture the interactions among objects from the visual and textual content to identify the differences among similar video moment candidates.

Cross-Modal Retrieval Graph Matching +4

Exploring Deep Learning for Joint Audio-Visual Lip Biometrics

1 code implementation17 Apr 2021 Meng Liu, Longbiao Wang, Kong Aik Lee, Hanyi Zhang, Chang Zeng, Jianwu Dang

Audio-visual (AV) lip biometrics is a promising authentication technique that leverages the benefits of both the audio and visual modalities in speech communication.

Speaker Recognition

DIG: A Turnkey Library for Diving into Graph Deep Learning Research

1 code implementation23 Mar 2021 Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora Oztekin, Xuan Zhang, Shuiwang Ji

Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning.

Benchmarking Graph Generation +1

Spherical Message Passing for 3D Graph Networks

1 code implementation ICLR 2022 Yi Liu, Limei Wang, Meng Liu, Xuan Zhang, Bora Oztekin, Shuiwang Ji

Based on such observations, we propose the spherical message passing (SMP) as a novel and powerful scheme for 3D molecular learning.

Drug Discovery Representation Learning

GraphEBM: Molecular Graph Generation with Energy-Based Models

1 code implementation ICLR Workshop EBM 2021 Meng Liu, Keqiang Yan, Bora Oztekin, Shuiwang Ji

We note that most existing approaches for molecular graph generation fail to guarantee the intrinsic property of permutation invariance, resulting in unexpected bias in generative models.

Graph Generation Molecular Graph Generation

Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery

1 code implementation2 Dec 2020 Zhengyang Wang, Meng Liu, Youzhi Luo, Zhao Xu, Yaochen Xie, Limei Wang, Lei Cai, Qi Qi, Zhuoning Yuan, Tianbao Yang, Shuiwang Ji

Here we develop a suite of comprehensive machine learning methods and tools spanning different computational models, molecular representations, and loss functions for molecular property prediction and drug discovery.

BIG-bench Machine Learning Drug Discovery +2

The Diversified Ensemble Neural Network

no code implementations NeurIPS 2020 Shaofeng Zhang, Meng Liu, Junchi Yan

Ensemble is a general way of improving the accuracy and stability of learning models, especially for the generalization ability on small datasets.

Frame-wise Cross-modal Matching for Video Moment Retrieval

1 code implementation22 Sep 2020 Haoyu Tang, Jihua Zhu, Meng Liu, Member, IEEE, Zan Gao, Zhiyong Cheng

Another contribution is that we propose an additional predictor to utilize the internal frames in the model training to improve the localization accuracy.

Boundary Detection Moment Retrieval +1

Towards Deeper Graph Neural Networks

3 code implementations18 Jul 2020 Meng Liu, Hongyang Gao, Shuiwang Ji

Based on our theoretical and empirical analysis, we propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.

Attribute Graph Neural Network +3

Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering

1 code implementation NeurIPS 2020 Meng Liu, David F. Gleich

For this problem, we propose a novel generalization of random walk, diffusion, or smooth function methods in the literature to a convex p-norm cut function.

Clustering Community Detection +1

Non-Local Graph Neural Networks

1 code implementation29 May 2020 Meng Liu, Zhengyang Wang, Shuiwang Ji

Modern graph neural networks (GNNs) learn node embeddings through multilayer local aggregation and achieve great success in applications on assortative graphs.

Node Classification on Non-Homophilic (Heterophilic) Graphs

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 Graph Neural Network +2

Cost-Sensitive Feature Selection by Optimizing F-Measures

no code implementations4 Apr 2019 Meng Liu, Chang Xu, Yong Luo, Chao Xu, Yonggang Wen, DaCheng Tao

Feature selection is beneficial for improving the performance of general machine learning tasks by extracting an informative subset from the high-dimensional features.

feature selection

Assessment of central serous chorioretinopathy (CSC) depicted on color fundus photographs using deep Learning

no code implementations14 Jan 2019 Yi Zhen, Hang Chen, Xu Zhang, Meng Liu, Xin Meng, Jian Zhang, Jiantao Pu

To investigate whether and to what extent central serous chorioretinopathy (CSC) depicted on color fundus photographs can be assessed using deep learning technology.

Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation

2 code implementations19 May 2018 Jichao Zhang, Yezhi Shu, Songhua Xu, Gongze Cao, Fan Zhong, Meng Liu, Xueying Qin

To overcome such a key limitation, we propose Sparsely Grouped Generative Adversarial Networks (SG-GAN) as a novel approach that can translate images on sparsely grouped datasets where only a few samples for training are labelled.

Attribute Image-to-Image Translation +3

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