Search Results for author: Tianrui Li

Found 59 papers, 16 papers with code

Personalized Federated Continual Learning via Multi-granularity Prompt

1 code implementation27 Jun 2024 Hao Yu, Xin Yang, Xin Gao, Yan Kang, Hao Wang, Junbo Zhang, Tianrui Li

In addition, we design a selective prompt fusion mechanism for aggregating knowledge of global prompts distilled from different clients.

Continual Learning Personalized Federated Learning

Adaptive Collaborative Correlation Learning-based Semi-Supervised Multi-Label Feature Selection

no code implementations18 Jun 2024 Yanyong Huang, Li Yang, Dongjie Wang, Ke Li, Xiuwen Yi, Fengmao Lv, Tianrui Li

Then, the instance correlation and label correlation are integrated into the proposed regression model to adaptively learn both the sample similarity graph and the label similarity graph, which mutually enhance feature selection performance.

feature selection Missing Labels +1

TDT Loss Takes It All: Integrating Temporal Dependencies among Targets into Non-Autoregressive Time Series Forecasting

no code implementations7 Jun 2024 Qi Xiong, Kai Tang, Minbo Ma, Jie Xu, Tianrui Li

Learning temporal dependencies among targets (TDT) benefits better time series forecasting, where targets refer to the predicted sequence.

Time Series Time Series Forecasting

A Timely Survey on Vision Transformer for Deepfake Detection

no code implementations14 May 2024 Zhikan Wang, Zhongyao Cheng, Jiajie Xiong, Xun Xu, Tianrui Li, Bharadwaj Veeravalli, Xulei Yang

In recent years, the rapid advancement of deepfake technology has revolutionized content creation, lowering forgery costs while elevating quality.

DeepFake Detection Face Swapping

FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer

no code implementations4 May 2024 Xin Gao, Xin Yang, Hao Yu, Yan Kang, Tianrui Li

Federated Class-Incremental Learning (FCIL) focuses on continually transferring the previous knowledge to learn new classes in dynamic Federated Learning (FL).

Class Incremental Learning Federated Learning +2

Dynamic Backtracking in GFlowNets: Enhancing Decision Steps with Reward-Dependent Adjustment Mechanisms

no code implementations8 Apr 2024 Shuai Guo, Jielei Chu, Lei Zhu, Zhaoyu Li, Tianrui Li

This paper introduces a novel variant of GFNs, the Dynamic Backtracking GFN (DB-GFN), which improves the adaptability of decision-making steps through a reward-based dynamic backtracking mechanism.

Decision Making

Continual Learning for Smart City: A Survey

no code implementations1 Apr 2024 Li Yang, Zhipeng Luo, Shiming Zhang, Fei Teng, Tianrui Li

We believe this survey can help relevant researchers quickly familiarize themselves with the current state of continual learning research used in smart city development and direct them to future research trends.

Continual Learning Federated Learning +1

Open Continual Feature Selection via Granular-Ball Knowledge Transfer

1 code implementation15 Mar 2024 Xuemei Cao, Xin Yang, Shuyin Xia, Guoyin Wang, Tianrui Li

To this end, the proposed CFS method combines the strengths of continual learning (CL) with granular-ball computing (GBC), which focuses on constructing a granular-ball knowledge base to detect unknown classes and facilitate the transfer of previously learned knowledge for further feature selection.

Continual Learning feature selection +1

Adversarial Training with OCR Modality Perturbation for Scene-Text Visual Question Answering

1 code implementation14 Mar 2024 Zhixuan Shen, Haonan Luo, Sijia Li, Tianrui Li

Scene-Text Visual Question Answering (ST-VQA) aims to understand scene text in images and answer questions related to the text content.

Optical Character Recognition Optical Character Recognition (OCR) +2

Self-supervised Contrastive Learning for Implicit Collaborative Filtering

no code implementations12 Mar 2024 Shipeng Song, Bin Liu, Fei Teng, Tianrui Li

Contrastive learning-based recommendation algorithms have significantly advanced the field of self-supervised recommendation, particularly with BPR as a representative ranking prediction task that dominates implicit collaborative filtering.

Collaborative Filtering Contrastive Learning +1

A Survey of Route Recommendations: Methods, Applications, and Opportunities

no code implementations1 Mar 2024 Shiming Zhang, Zhipeng Luo, Li Yang, Fei Teng, Tianrui Li

Our survey offers a comprehensive review of route recommendation work based on urban computing.

Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook

2 code implementations29 Feb 2024 Xingchen Zou, Yibo Yan, Xixuan Hao, Yuehong Hu, Haomin Wen, Erdong Liu, Junbo Zhang, Yong Li, Tianrui Li, Yu Zheng, Yuxuan Liang

As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for sustainable development by harnessing the power of cross-domain data fusion from diverse sources (e. g., geographical, traffic, social media, and environmental data) and modalities (e. g., spatio-temporal, visual, and textual modalities).

Learning Contrastive Feature Representations for Facial Action Unit Detection

1 code implementation9 Feb 2024 Ziqiao Shang, Bin Liu, Fengmao Lv, Fei Teng, Tianrui Li

Facial action unit (AU) detection has long encountered the challenge of detecting subtle feature differences when AUs activate.

Action Unit Detection Binary Classification +2

Hierarchical Continual Reinforcement Learning via Large Language Model

no code implementations25 Jan 2024 Chaofan Pan, Xin Yang, Hao Wang, Wei Wei, Tianrui Li

Despite the progress in continual reinforcement learning (CRL), existing methods often suffer from insufficient knowledge transfer, particularly when the tasks are diverse.

Language Modelling Large Language Model +3

Unified View Imputation and Feature Selection Learning for Incomplete Multi-view Data

no code implementations19 Jan 2024 Yanyong Huang, Zongxin Shen, Tianrui Li, Fengmao Lv

UNIFIER explores the local structure of multi-view data by adaptively learning similarity-induced graphs from both the sample and feature spaces.

feature selection Imputation

Federated Continual Learning via Knowledge Fusion: A Survey

no code implementations27 Dec 2023 Xin Yang, Hao Yu, Xin Gao, Hao Wang, Junbo Zhang, Tianrui Li

The key objective of FCL is to fuse heterogeneous knowledge from different clients and retain knowledge of previous tasks while learning on new ones.

Continual Learning Federated Learning

Learning to Prompt Knowledge Transfer for Open-World Continual Learning

no code implementations22 Dec 2023 Yujie Li, Xin Yang, Hao Wang, Xiangkun Wang, Tianrui Li

This paper studies the problem of continual learning in an open-world scenario, referred to as Open-world Continual Learning (OwCL).

Continual Learning Transfer Learning

Hi-ResNet: A High-Resolution Remote Sensing Network for Semantic Segmentation

no code implementations22 May 2023 Yuxia Chen, Pengcheng Fang, Jianhui Yu, Xiaoling Zhong, XiaoMing Zhang, Tianrui Li

In this work, we solve the above-mentioned problems by proposing a High-resolution remote sensing network (Hi-ResNet) with efficient network structure designs, which consists of a funnel module, a multi-branch module with stacks of information aggregation (IA) blocks, and a feature refinement module, sequentially, and Class-agnostic Edge Aware (CEA) loss.

Semantic Segmentation

Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training

1 code implementation20 Mar 2023 Yongyi Su, Xun Xu, Tianrui Li, Kui Jia

Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full source domain data is not available, and instant inference on the target domain is required.

Benchmarking Clustering

Bayesian Self-Supervised Contrastive Learning

1 code implementation27 Jan 2023 Bin Liu, Bang Wang, Tianrui Li

Recent years have witnessed many successful applications of contrastive learning in diverse domains, yet its self-supervised version still remains many exciting challenges.

Contrastive Learning

Boosting Single Image Super-Resolution via Partial Channel Shifting

1 code implementation ICCV 2023 XiaoMing Zhang, Tianrui Li, Xiaole Zhao

Specifically, it is inspired by the temporal shifting in video understanding and displaces part of the channels along the spatial dimensions, thus allowing the effective receptive field to be amplified and the feature diversity to be augmented at almost zero cost.

Diversity Image Super-Resolution +1

A Missing Value Filling Model Based on Feature Fusion Enhanced Autoencoder

no code implementations29 Aug 2022 Xinyao Liu, Shengdong Du, Tianrui Li, Fei Teng, Yan Yang

We first incorporate into an autoencoder a hidden layer that consists of de-tracking neurons and radial basis function neurons, which can enhance the ability of learning interrelated features and common features.


C$^{2}$IMUFS: Complementary and Consensus Learning-based Incomplete Multi-view Unsupervised Feature Selection

no code implementations20 Aug 2022 Yanyong Huang, Zongxin Shen, Yuxin Cai, Xiuwen Yi, Dongjie Wang, Fengmao Lv, Tianrui Li

Besides, learning the complete similarity graph, as an important promising technology in existing MUFS methods, cannot achieve due to the missing views.

feature selection

DRAformer: Differentially Reconstructed Attention Transformer for Time-Series Forecasting

no code implementations11 Jun 2022 Benhan Li, Shengdong Du, Tianrui Li, Jie Hu, Zhen Jia

Time-series forecasting plays an important role in many real-world scenarios, such as equipment life cycle forecasting, weather forecasting, and traffic flow forecasting.

Decoder Time Series +2

Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction

no code implementations6 Apr 2022 Peng Xie, Minbo Ma, Tianrui Li, Shenggong Ji, Shengdong Du, Zeng Yu, Junbo Zhang

Second, we employ a dynamic graph relationship learning module to learn dynamic spatial relationships between metro stations without a predefined graph adjacency matrix.

Management Relation +2

Incremental Unsupervised Feature Selection for Dynamic Incomplete Multi-view Data

no code implementations5 Apr 2022 Yanyong Huang, Kejun Guo, Xiuwen Yi, Zhong Li, Tianrui Li

To address these issues, we propose an Incremental Incomplete Multi-view Unsupervised Feature Selection method (I$^2$MUFS) on incomplete multi-view streaming data.

Clustering feature selection

Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting

no code implementations25 Feb 2022 Jiabin Tang, Tang Qian, Shijing Liu, Shengdong Du, Jie Hu, Tianrui Li

Accurate traffic forecasting, the foundation of intelligent transportation systems (ITS), has never been more significant than nowadays due to the prosperity of smart cities and urban computing.

Benchmarking Graph Neural Network +1

A Differential Attention Fusion Model Based on Transformer for Time Series Forecasting

no code implementations23 Feb 2022 Benhan Li, Shengdong Du, Tianrui Li

Time series forecasting is widely used in the fields of equipment life cycle forecasting, weather forecasting, traffic flow forecasting, and other fields.

Time Series Time Series Forecasting +1

HiSTGNN: Hierarchical Spatio-temporal Graph Neural Networks for Weather Forecasting

no code implementations22 Jan 2022 Minbo Ma, Peng Xie, Fei Teng, Tianrui Li, Bin Wang, Shenggong Ji, Junbo Zhang

In this paper, we propose a novel Hierarchical Spatio-Temporal Graph Neural Network (HiSTGNN) to model cross-regional spatio-temporal correlations among meteorological variables in multiple stations.

Graph Learning Graph Neural Network +4

Expanding Large Pre-Trained Unimodal Models With Multimodal Information Injection for Image-Text Multimodal Classification

no code implementations CVPR 2022 Tao Liang, Guosheng Lin, Mingyang Wan, Tianrui Li, Guojun Ma, Fengmao Lv

Through the proposed MI2P unit, we can inject the language information into the vision backbone by attending the word-wise textual features to different visual channels, as well as inject the visual information into the language backbone by attending the channel-wise visual features to different textual words.

Unsupervised feature selection via self-paced learning and low-redundant regularization

no code implementations14 Dec 2021 Weiyi Li, Hongmei Chen, Tianrui Li, Jihong Wan, Binbin Sang

In this study, an unsupervised feature selection is proposed by integrating the framework of self-paced learning and subspace learning.

feature selection

ScaleVLAD: Improving Multimodal Sentiment Analysis via Multi-Scale Fusion of Locally Descriptors

no code implementations2 Dec 2021 Huaishao Luo, Lei Ji, Yanyong Huang, Bin Wang, Shenggong Ji, Tianrui Li

This paper proposes a fusion model named ScaleVLAD to gather multi-Scale representation from text, video, and audio with shared Vectors of Locally Aggregated Descriptors to improve unaligned multimodal sentiment analysis.

Multimodal Sentiment Analysis

CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval

5 code implementations18 Apr 2021 Huaishao Luo, Lei Ji, Ming Zhong, Yang Chen, Wen Lei, Nan Duan, Tianrui Li

In this paper, we propose a CLIP4Clip model to transfer the knowledge of the CLIP model to video-language retrieval in an end-to-end manner.

Text Retrieval Text to Video Retrieval +3

Adaptive Graph-based Generalized Regression Model for Unsupervised Feature Selection

no code implementations27 Dec 2020 Yanyong Huang, Zongxin Shen, Fuxu Cai, Tianrui Li, Fengmao Lv

Other existing methods choose the discriminative features with low redundancy by constructing the graph matrix on the original feature space.

Clustering feature selection +2

Fairness and Accuracy in Federated Learning

no code implementations18 Dec 2020 Wei Huang, Tianrui Li, Dexian Wang, Shengdong Du, Junbo Zhang

An appropriate weight selection algorithm that combines the information quantity of training accuracy and training frequency to measure the weights is proposed.

Fairness Federated Learning

Distributional Discrepancy: A Metric for Unconditional Text Generation

1 code implementation4 May 2020 Ping Cai, Xingyuan Chen, Peng Jin, Hongjun Wang, Tianrui Li

The purpose of unconditional text generation is to train a model with real sentences, then generate novel sentences of the same quality and diversity as the training data.

Diversity Language Modelling +1

Micro-supervised Disturbance Learning: A Perspective of Representation Probability Distribution

no code implementations13 Mar 2020 Jielei Chu, Jing Liu, Hongjun Wang, Meng Hua, Zhiguo Gong, Tianrui Li

To explore the representation learning capability under the continuous stimulation of the SPI, we present a deep Micro-supervised Disturbance Learning (Micro-DL) framework based on the Micro-DGRBM and Micro-DRBM models and compare it with a similar deep structure which has not any external stimulation.

Representation Learning

UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation

2 code implementations15 Feb 2020 Huaishao Luo, Lei Ji, Botian Shi, Haoyang Huang, Nan Duan, Tianrui Li, Jason Li, Taroon Bharti, Ming Zhou

However, most of the existing multimodal models are pre-trained for understanding tasks, leading to a pretrain-finetune discrepancy for generation tasks.

Ranked #2 on Action Segmentation on COIN (using extra training data)

Action Segmentation Decoder +3

Learning with Noisy Labels for Sentence-level Sentiment Classification

no code implementations IJCNLP 2019 Hao Wang, Bing Liu, Chaozhuo Li, Yan Yang, Tianrui Li

We propose a novel DNN model called NetAb (as shorthand for convolutional neural Networks with Ab-networks) to handle noisy labels during training.

Classification General Classification +4

Multi-local Collaborative AutoEncoder

no code implementations12 Jun 2019 Jielei Chu, Hongjun Wang, Jing Liu, Zhiguo Gong, Tianrui Li

In mcrRBM and mcrGRBM models, the structure and multi-local collaborative relationships of unlabeled data are integrated into their encoding procedure.

Clustering Representation Learning

DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction

1 code implementation ACL 2019 Huaishao Luo, Tianrui Li, Bing Liu, Junbo Zhang

This paper focuses on two related subtasks of aspect-based sentiment analysis, namely aspect term extraction and aspect sentiment classification, which we call aspect term-polarity co-extraction.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +3

Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting

3 code implementations22 Dec 2018 Bin Wang, Jie Lu, Zheng Yan, Huaishao Luo, Tianrui Li, Yu Zheng, Guangquan Zhang

We cast the weather forecasting problem as an end-to-end deep learning problem and solve it by proposing a novel negative log-likelihood error (NLE) loss function.

BIG-bench Machine Learning Uncertainty Quantification +1

Deep Air Quality Forecasting Using Hybrid Deep Learning Framework

no code implementations12 Dec 2018 Shengdong Du, Tianrui Li, Yan Yang, Shi-Jinn Horng

Air quality forecasting has been regarded as the key problem of air pollution early warning and control management.

Management Time Series +1

Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM

no code implementations5 Dec 2018 Jielei Chu, Hongjun Wang, Jing Liu, Zhiguo Gong, Tianrui Li

In this paper, we present a novel unsupervised feature learning architecture, which consists of a multi-clustering integration module and a variant of RBM termed multi-clustering integration RBM (MIRBM).


Improving Aspect Term Extraction with Bidirectional Dependency Tree Representation

1 code implementation21 May 2018 Huaishao Luo, Tianrui Li, Bing Liu, Bin Wang, Herwig Unger

The key idea is to explicitly incorporate both representations gained separately from the bottom-up and top-down propagation on the given dependency syntactic tree.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1

Three-Stream Convolutional Networks for Video-based Person Re-Identification

no code implementations22 Nov 2017 Zeng Yu, Tianrui Li, Ning Yu, Xun Gong, Ke Chen, Yi Pan

This paper aims to develop a new architecture that can make full use of the feature maps of convolutional networks.

Video-Based Person Re-Identification

Reconstruction of Hidden Representation for Robust Feature Extraction

no code implementations8 Oct 2017 Zeng Yu, Tianrui Li, Ning Yu, Yi Pan, Hongmei Chen, Bing Liu

We believe that minimizing the reconstruction error of the hidden representation is more robust than minimizing the Frobenius norm of the Jacobian matrix of the hidden representation.

Denoising Representation Learning

Restricted Boltzmann Machines with Gaussian Visible Units Guided by Pairwise Constraints

no code implementations13 Jan 2017 Jielei Chu, Hongjun Wang, Hua Meng, Peng Jin, Tianrui Li

To enhance the expression ability of traditional RBMs, in this paper, we propose pairwise constraints restricted Boltzmann machine with Gaussian visible units (pcGRBM) model, in which the learning procedure is guided by pairwise constraints and the process of encoding is conducted under these guidances.


Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks

no code implementations10 Jan 2017 Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, Xiuwen Yi, Tianrui Li

We propose a deep-learning-based approach, called ST-ResNet, to collectively forecast two types of crowd flows (i. e. inflow and outflow) in each and every region of a city.


Parallel Large-Scale Attribute Reduction on Cloud Systems

no code implementations6 Oct 2016 Junbo Zhang, Tianrui Li, Yi Pan

The rapid growth of emerging information technologies and application patterns in modern society, e. g., Internet, Internet of Things, Cloud Computing and Tri-network Convergence, has caused the advent of the era of big data.

Attribute Cloud Computing +1

ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data

no code implementations IJCAI 2016 2016 Xiuwen Yi, Yu Zheng, Junbo Zhang, Tianrui Li

In this paper, we propose a spatio-temporal multi-view-based learning (ST-MVL) method to collectively fill missing readings in a collection of geosensory time series data, considering 1) the temporal correlation between readings at different timestamps in the same series and 2) the spatial correlation between different time series.

Collaborative Filtering Multivariate Time Series Imputation +3

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