Search Results for author: Xinwang Liu

Found 90 papers, 38 papers with code

Part-aware Shape Generation with Latent 3D Diffusion of Neural Voxel Fields

no code implementations2 May 2024 Yuhang Huang, SHilong Zou, Xinwang Liu, Kai Xu

This paper presents a novel latent 3D diffusion model for the generation of neural voxel fields, aiming to achieve accurate part-aware structures.

Decoder

AdvLoRA: Adversarial Low-Rank Adaptation of Vision-Language Models

no code implementations20 Apr 2024 Yuheng Ji, Yue Liu, Zhicheng Zhang, Zhao Zhang, YuTing Zhao, Gang Zhou, Xingwei Zhang, Xinwang Liu, Xiaolong Zheng

Different from LoRA, we improve the efficiency and robustness of adversarial adaptation by designing a novel reparameterizing method based on parameter clustering and parameter alignment.

Post-Semantic-Thinking: A Robust Strategy to Distill Reasoning Capacity from Large Language Models

no code implementations14 Apr 2024 Xiaoshu Chen, Sihang Zhou, Ke Liang, Xinwang Liu

Chain of thought finetuning aims to endow small student models with reasoning capacity to improve their performance towards a specific task by allowing them to imitate the reasoning procedure of large language models (LLMs) beyond simply predicting the answer to the question.

Hallucination

Efficient Prompt Tuning of Large Vision-Language Model for Fine-Grained Ship Classification

no code implementations13 Mar 2024 Long Lan, Fengxiang Wang, Shuyan Li, Xiangtao Zheng, Zengmao Wang, Xinwang Liu

Directly fine-tuning VLMs for RS-FGSC often encounters the challenge of overfitting the seen classes, resulting in suboptimal generalization to unseen classes, which highlights the difficulty in differentiating complex backgrounds and capturing distinct ship features.

Language Modelling Zero-Shot Learning

A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges

no code implementations7 Mar 2024 Wei Ju, Siyu Yi, Yifan Wang, Zhiping Xiao, Zhengyang Mao, Hourun Li, Yiyang Gu, Yifang Qin, Nan Yin, Senzhang Wang, Xinwang Liu, Xiao Luo, Philip S. Yu, Ming Zhang

To tackle these issues, substantial efforts have been devoted to improving the performance of GNN models in practical real-world scenarios, as well as enhancing their reliability and robustness.

Fraud Detection

Phrase Grounding-based Style Transfer for Single-Domain Generalized Object Detection

no code implementations2 Feb 2024 Hao Li, Wei Wang, Cong Wang, Zhigang Luo, Xinwang Liu, Kenli Li, Xiaochun Cao

Single-domain generalized object detection aims to enhance a model's generalizability to multiple unseen target domains using only data from a single source domain during training.

object-detection Object Detection +2

End-to-end Learnable Clustering for Intent Learning in Recommendation

1 code implementation11 Jan 2024 Yue Liu, Shihao Zhu, Jun Xia, Yingwei Ma, Jian Ma, Wenliang Zhong, Xinwang Liu, Guannan Zhang, Kejun Zhang

Concretely, we encode users' behavior sequences and initialize the cluster centers (latent intents) as learnable neurons.

Clustering Contrastive Learning +2

Learn from View Correlation: An Anchor Enhancement Strategy for Multi-view Clustering

no code implementations CVPR 2024 Suyuan Liu, Ke Liang, Zhibin Dong, Siwei Wang, Xihong Yang, Sihang Zhou, En Zhu, Xinwang Liu

By learning from view correlation we enhance the anchors of the current view using the relationships between anchors and samples on neighboring views thereby narrowing the spatial distribution of anchors on similar views.

Clustering

Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types

no code implementations28 Nov 2023 Dayu Hu, Ke Liang, Hao Yu, Xinwang Liu

This model leverages exogenous gene network information to facilitate the clustering process, generating discriminative representations.

Clustering Deep Clustering

Single-cell Multi-view Clustering via Community Detection with Unknown Number of Clusters

1 code implementation28 Nov 2023 Dayu Hu, Zhibin Dong, Ke Liang, Jun Wang, Siwei Wang, Xinwang Liu

To this end, we introduce scUNC, an innovative multi-view clustering approach tailored for single-cell data, which seamlessly integrates information from different views without the need for a predefined number of clusters.

Clustering Community Detection

MS-Former: Memory-Supported Transformer for Weakly Supervised Change Detection with Patch-Level Annotations

1 code implementation16 Nov 2023 Zhenglai Li, Chang Tang, Xinwang Liu, Changdong Li, Xianju Li, Wei zhang

How to capture the semantic variations associated with the changed and unchanged regions from the patch-level annotations to obtain promising change results is the critical challenge for the weakly supervised change detection task.

Change Detection

RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization

1 code implementation NeurIPS 2023 Siqi Shen, Chennan Ma, Chao Li, Weiquan Liu, Yongquan Fu, Songzhu Mei, Xinwang Liu, Cheng Wang

Multi-agent systems are characterized by environmental uncertainty, varying policies of agents, and partial observability, which result in significant risks.

Multi-agent Reinforcement Learning reinforcement-learning

Rethinking SIGN Training: Provable Nonconvex Acceleration without First- and Second-Order Gradient Lipschitz

no code implementations23 Oct 2023 Tao Sun, Congliang Chen, Peng Qiao, Li Shen, Xinwang Liu, Dongsheng Li

Sign-based stochastic methods have gained attention due to their ability to achieve robust performance despite using only the sign information for parameter updates.

Contrastive Continual Multi-view Clustering with Filtered Structural Fusion

no code implementations26 Sep 2023 Xinhang Wan, Jiyuan Liu, Hao Yu, Ao Li, Xinwang Liu, Ke Liang, Zhibin Dong, En Zhu

Precisely, considering that data correlations play a vital role in clustering and prior knowledge ought to guide the clustering process of a new view, we develop a data buffer with fixed size to store filtered structural information and utilize it to guide the generation of a robust partition matrix via contrastive learning.

Clustering Contrastive Learning +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

Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning

1 code implementation12 Sep 2023 Jingcan Duan, Pei Zhang, Siwei Wang, Jingtao Hu, Hu Jin, Jiaxin Zhang, Haifang Zhou, Xinwang Liu

Finally, the model is refined with the only input of reliable normal nodes and learns a more accurate estimate of normality so that anomalous nodes can be more easily distinguished.

Contrastive Learning Graph Anomaly Detection

Efficient Multi-View Graph Clustering with Local and Global Structure Preservation

1 code implementation31 Aug 2023 Yi Wen, Suyuan Liu, Xinhang Wan, Siwei Wang, Ke Liang, Xinwang Liu, Xihong Yang, Pei Zhang

Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability to capture complementary structural information across multiple views.

Clustering Graph Clustering +1

Scalable Incomplete Multi-View Clustering with Structure Alignment

1 code implementation31 Aug 2023 Yi Wen, Siwei Wang, Ke Liang, Weixuan Liang, Xinhang Wan, Xinwang Liu, Suyuan Liu, Jiyuan Liu, En Zhu

Although several anchor-based IMVC methods have been proposed to process the large-scale incomplete data, they still suffer from the following drawbacks: i) Most existing approaches neglect the inter-view discrepancy and enforce cross-view representation to be consistent, which would corrupt the representation capability of the model; ii) Due to the samples disparity between different views, the learned anchor might be misaligned, which we referred as the Anchor-Unaligned Problem for Incomplete data (AUP-ID).

Clustering graph construction +2

DealMVC: Dual Contrastive Calibration for Multi-view Clustering

1 code implementation17 Aug 2023 Xihong Yang, Jiaqi Jin, Siwei Wang, Ke Liang, Yue Liu, Yi Wen, Suyuan Liu, Sihang Zhou, Xinwang Liu, En Zhu

Then, a global contrastive calibration loss is proposed by aligning the view feature similarity graph and the high-confidence pseudo-label graph.

Clustering Pseudo Label

CONVERT:Contrastive Graph Clustering with Reliable Augmentation

2 code implementations17 Aug 2023 Xihong Yang, Cheng Tan, Yue Liu, Ke Liang, Siwei Wang, Sihang Zhou, Jun Xia, Stan Z. Li, Xinwang Liu, En Zhu

To address these problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT).

Clustering Contrastive Learning +4

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

Unpaired Multi-View Graph Clustering with Cross-View Structure Matching

1 code implementation7 Jul 2023 Yi Wen, Siwei Wang, Qing Liao, Weixuan Liang, Ke Liang, Xinhang Wan, Xinwang Liu

Besides, our UPMGC-SM is a unified framework for both the fully and partially unpaired multi-view graph clustering.

Clustering Graph Clustering

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

Medical Federated Model with Mixture of Personalized and Sharing Components

1 code implementation26 Jun 2023 Yawei Zhao, Qinghe Liu, Xinwang Liu, Kunlun He

Comparing with 13 existing related methods, the proposed method successfully achieves the best model performance, and meanwhile up to 60% improvement of communication efficiency.

Federated Learning Tumor Segmentation

Decoupled Diffusion Models: Simultaneous Image to Zero and Zero to Noise

1 code implementation23 Jun 2023 Yuhang Huang, Zheng Qin, Xinwang Liu, Kai Xu

We propose decoupled diffusion models (DDMs) for high-quality (un)conditioned image generation in less than 10 function evaluations.

Denoising Edge Detection +3

Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation

1 code implementation14 Jun 2023 Xiao He, Chang Tang, Xinwang Liu, Wei zhang, Kun Sun, Jiangfeng Xu

S2ADet comprises a hyperspectral information decoupling (HID) module, a two-stream feature extraction network, and a one-stage detection head.

Object object-detection +1

One-step Multi-view Clustering with Diverse Representation

no code implementations8 Jun 2023 Xinhang Wan, Jiyuan Liu, Xinwang Liu, Siwei Wang, Yi Wen, Tianjiao Wan, Li Shen, En Zhu

In light of this, we propose a one-step multi-view clustering with diverse representation method, which incorporates multi-view learning and $k$-means into a unified framework.

Clustering MULTI-VIEW LEARNING +1

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

Fast Continual Multi-View Clustering with Incomplete Views

no code implementations4 Jun 2023 Xinhang Wan, Bin Xiao, Xinwang Liu, Jiyuan Liu, Weixuan Liang, En Zhu

Such an incomplete continual data problem (ICDP) in MVC is tough to solve since incomplete information with continual data increases the difficulty of extracting consistent and complementary knowledge among views.

Clustering

Dink-Net: Neural Clustering on Large Graphs

3 code implementations28 May 2023 Yue Liu, Ke Liang, Jun Xia, Sihang Zhou, Xihong Yang, Xinwang Liu, Stan Z. Li

Subsequently, the clustering distribution is optimized by minimizing the proposed cluster dilation loss and cluster shrink loss in an adversarial manner.

Clustering Graph Clustering +1

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

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

RARE: Robust Masked Graph Autoencoder

no code implementations4 Apr 2023 Wenxuan Tu, Qing Liao, Sihang Zhou, Xin Peng, Chuan Ma, Zhe Liu, Xinwang Liu, Zhiping Cai

To address this issue, we propose a novel SGP method termed Robust mAsked gRaph autoEncoder (RARE) to improve the certainty in inferring masked data and the reliability of the self-supervision mechanism by further masking and reconstructing node samples in the high-order latent feature space.

GANN: Graph Alignment Neural Network for Semi-Supervised Learning

no code implementations14 Mar 2023 Linxuan Song, Wenxuan Tu, Sihang Zhou, Xinwang Liu, En Zhu

Graph neural networks (GNNs) have been widely investigated in the field of semi-supervised graph machine learning.

Attribute Node Classification

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

Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network

no code implementations15 Feb 2023 Wenxuan Tu, Bin Xiao, Xinwang Liu, Sihang Zhou, Zhiping Cai, Jieren Cheng

With the development of various applications, such as social networks and knowledge graphs, graph data has been ubiquitous in the real world.

Attribute Imputation +1

Auto-weighted Multi-view Clustering for Large-scale Data

1 code implementation21 Jan 2023 Xinhang Wan, Xinwang Liu, Jiyuan Liu, Siwei Wang, Yi Wen, Weixuan Liang, En Zhu, Zhe Liu, Lu Zhou

Multi-view clustering has gained broad attention owing to its capacity to exploit complementary information across multiple data views.

Clustering

Cluster-guided Contrastive Graph Clustering Network

1 code implementation3 Jan 2023 Xihong Yang, Yue Liu, Sihang Zhou, Siwei Wang, Wenxuan Tu, Qun Zheng, Xinwang Liu, Liming Fang, En Zhu

Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views.

Clustering Contrastive Learning +1

Cross-view Topology Based Consistent and Complementary Information for Deep Multi-view Clustering

no code implementations ICCV 2023 Zhibin Dong, Siwei Wang, Jiaqi Jin, Xinwang Liu, En Zhu

However, most existing deep clustering approaches are dedicated to merging and exploring the consistent latent representation across multiple views while overlooking the abundant complementary information in each view.

Clustering Deep Clustering +2

Hard Sample Aware Network for Contrastive Deep Graph Clustering

2 code implementations16 Dec 2022 Yue Liu, Xihong Yang, Sihang Zhou, Xinwang Liu, Zhen Wang, Ke Liang, Wenxuan Tu, Liang Li, Jingcan Duan, Cancan Chen

Moreover, under the guidance of the carefully collected high-confidence clustering information, our proposed weight modulating function will first recognize the positive and negative samples and then dynamically up-weight the hard sample pairs while down-weighting the easy ones.

Attribute Clustering +1

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

Attribute Graph Clustering via Learnable Augmentation

1 code implementation7 Dec 2022 Xihong Yang, Yue Liu, Ke Liang, Sihang Zhou, Xinwang Liu, En Zhu

To this end, we propose an Attribute Graph Clustering method via Learnable Augmentation (\textbf{AGCLA}), which introduces learnable augmentors for high-quality and suitable augmented samples for CDGC.

Attribute Clustering +4

Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure

no code implementations19 Nov 2022 Ke Liang, Yue Liu, Sihang Zhou, Wenxuan Tu, Yi Wen, Xihong Yang, Xiangjun Dong, Xinwang Liu

To this end, we propose a knowledge graph contrastive learning framework based on relation-symmetrical structure, KGE-SymCL, which mines symmetrical structure information in KGs to enhance the discriminative ability of KGE models.

Contrastive Learning Graph Learning +5

Late Fusion Multi-view Clustering via Global and Local Alignment Maximization

1 code implementation2 Aug 2022 Siwei Wang, Xinwang Liu, En Zhu

It optimally fuses multiple source information in partition level from each individual view, and maximally aligns the consensus partition with these weighted base ones.

Clustering

Multiple Kernel Clustering with Dual Noise Minimization

no code implementations13 Jul 2022 Junpu Zhang, Liang Li, Siwei Wang, Jiyuan Liu, Yue Liu, Xinwang Liu, En Zhu

As a representative, late fusion MKC first decomposes the kernels into orthogonal partition matrices, then learns a consensus one from them, achieving promising performance recently.

Clustering

Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph

1 code implementation5 Jul 2022 Liang Li, Siwei Wang, Xinwang Liu, En Zhu, Li Shen, Kenli Li, Keqin Li

Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels.

Clustering

Mixed Graph Contrastive Network for Semi-Supervised Node Classification

no code implementations6 Jun 2022 Xihong Yang, Yue Liu, Sihang Zhou, Xinwang Liu, En Zhu

Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised node classification in recent years.

Classification Contrastive Learning +4

Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences

1 code implementation30 May 2022 Siwei Wang, Xinwang Liu, Suyuan Liu, Jiaqi Jin, Wenxuan Tu, Xinzhong Zhu, En Zhu

Under multi-view scenarios, generating correct correspondences could be extremely difficult since anchors are not consistent in feature dimensions.

Clustering Graph Clustering

Simple Contrastive Graph Clustering

no code implementations11 May 2022 Yue Liu, Xihong Yang, Sihang Zhou, Xinwang Liu

To solve this problem, we propose a Simple Contrastive Graph Clustering (SCGC) algorithm to improve the existing methods from the perspectives of network architecture, data augmentation, and objective function.

Clustering Contrastive Learning +4

DisARM: Displacement Aware Relation Module for 3D Detection

no code implementations CVPR 2022 Yao Duan, Chenyang Zhu, Yuqing Lan, Renjiao Yi, Xinwang Liu, Kai Xu

However, adopting relations between all the object or patch proposals for detection is inefficient, and an imbalanced combination of local and global relations brings extra noise that could mislead the training.

3D Object Detection object-detection +1

Improved Dual Correlation Reduction Network

no code implementations25 Feb 2022 Yue Liu, Sihang Zhou, Xinwang Liu, Wenxuan Tu, Xihong Yang

Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different clusters without human annotations, is a fundamental yet challenging task.

Clustering Feature Correlation +1

Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning

no code implementations24 Feb 2022 Xihong Yang, Xiaochang Hu, Sihang Zhou, Xinwang Liu, En Zhu

Specifically, the proposed algorithm outperforms the second best algorithm (Comatch) with 5. 3% by achieving 88. 73% classification accuracy when only two labels are available for each class on the CIFAR-10 dataset.

Contrastive Learning Data Augmentation

High-order Correlation Preserved Incomplete Multi-view Subspace Clustering

3 code implementations IEEE Transactions on Image Processing 2022 Zhenglai Li, Chang Tang, Xiao Zheng, Xinwang Liu, Senior Member, Wei zhang, Member, IEEE, and En Zhu

Specifically, multiple affinity matrices constructed from the incomplete multi-view data are treated as a thirdorder low rank tensor with a tensor factorization regularization which preserves the high-order view correlation and sample correlation.

Clustering Incomplete multi-view clustering +2

Deep Graph Clustering via Dual Correlation Reduction

2 code implementations29 Dec 2021 Yue Liu, Wenxuan Tu, Sihang Zhou, Xinwang Liu, Linxuan Song, Xihong Yang, En Zhu

To address this issue, we propose a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) by reducing information correlation in a dual manner.

Clustering Feature Correlation +1

Siamese Attribute-missing Graph Auto-encoder

no code implementations9 Dec 2021 Wenxuan Tu, Sihang Zhou, Yue Liu, Xinwang Liu

First, we entangle the attribute embedding and structure embedding by introducing a siamese network structure to share the parameters learned by both processes, which allows the network training to benefit from more abundant and diverse information.

Attribute Graph Representation Learning

Video Abnormal Event Detection by Learning to Complete Visual Cloze Tests

1 code implementation5 Aug 2021 Siqi Wang, Guang Yu, Zhiping Cai, Xinwang Liu, En Zhu, Jianping Yin

With each patch and the patch sequence of a STC compared to a visual "word" and "sentence" respectively, we deliberately erase a certain "word" (patch) to yield a VCT.

Cloze Test Event Detection +2

Tensor-Based Multi-View Block-Diagonal Structure Diffusion for Clustering Incomplete Multi-View Data

1 code implementation IEEE International Conference on Multimedia and Expo 2021 Zhenglai Li, Chang Tang, Xinwang Liu, Xiao Zheng, Wei zhang, En Zhu

In this paper, we propose a novel incomplete multi-view clustering method, in which a tensor nuclear norm regularizer elegantly diffuses the information of multi-view block-diagonal structure across different views.

Clustering Incomplete multi-view clustering

Consensus Graph Learning for Multi-view Clustering

1 code implementation IEEE Transactions on Multimedia 2021 Zhenglai Li, Chang Tang, Xinwang Liu, Xiao Zheng, Guanghui Yue, Wei zhang

Furthermore, we unify the spectral embedding and low rank tensor learning into a unified optimization framework to determine the spectral embedding matrices and tensor representation jointly.

Clustering Graph Learning

Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement

no code implementations1 May 2021 Chen Zhang, Siwei Wang, Wenxuan Tu, Pei Zhang, Xinwang Liu, Changwang Zhang, Bo Yuan

Multi-view clustering is an important yet challenging task in machine learning and data mining community.

Clustering

Multi-view Deep One-class Classification: A Systematic Exploration

no code implementations27 Apr 2021 Siqi Wang, Jiyuan Liu, Guang Yu, Xinwang Liu, Sihang Zhou, En Zhu, Yuexiang Yang, Jianping Yin

Third, to remedy the problem that limited benchmark datasets are available for multi-view deep OCC, we extensively collect existing public data and process them into more than 30 new multi-view benchmark datasets via multiple means, so as to provide a publicly available evaluation platform for multi-view deep OCC.

Classification General Classification +1

One-Pass Multi-View Clustering for Large-Scale Data

no code implementations ICCV 2021 Jiyuan Liu, Xinwang Liu, Yuexiang Yang, Li Liu, Siqi Wang, Weixuan Liang, Jiangyong Shi

In this way, the generated partition can guide multi-view matrix factorization to produce more purposive coefficient matrix which, as a feedback, improves the quality of partition.

Clustering

Localized Simple Multiple Kernel K-Means

1 code implementation ICCV 2021 Xinwang Liu, Sihang Zhou, Li Liu, Chang Tang, Siwei Wang, Jiyuan Liu, Yi Zhang

After that, we theoretically show that the objective of SimpleMKKM is a special case of this local kernel alignment criterion with normalizing each base kernel matrix.

Clustering

Deep Fusion Clustering Network

1 code implementation15 Dec 2020 Wenxuan Tu, Sihang Zhou, Xinwang Liu, Xifeng Guo, Zhiping Cai, En Zhu, Jieren Cheng

Specifically, in our network, an interdependency learning-based Structure and Attribute Information Fusion (SAIF) module is proposed to explicitly merge the representations learned by an autoencoder and a graph autoencoder for consensus representation learning.

Attribute Clustering +3

A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-Identification

1 code implementation5 Sep 2020 Haocong Rao, Siqi Wang, Xiping Hu, Mingkui Tan, Yi Guo, Jun Cheng, Xinwang Liu, Bin Hu

This paper proposes a self-supervised gait encoding approach that can leverage unlabeled skeleton data to learn gait representations for person Re-ID.

Contrastive Learning Person Re-Identification +2

Structured Graph Learning for Clustering and Semi-supervised Classification

no code implementations31 Aug 2020 Zhao Kang, Chong Peng, Qiang Cheng, Xinwang Liu, Xi Peng, Zenglin Xu, Ling Tian

Furthermore, most existing graph-based methods conduct clustering and semi-supervised classification on the graph learned from the original data matrix, which doesn't have explicit cluster structure, thus they might not achieve the optimal performance.

Classification Clustering +2

Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrix

no code implementations31 Aug 2020 Weixuan Liang, Sihang Zhou, Jian Xiong, Xinwang Liu, Siwei Wang, En Zhu, Zhiping Cai, Xin Xu

Multi-view spectral clustering can effectively reveal the intrinsic cluster structure among data by performing clustering on the learned optimal embedding across views.

Clustering Vocal Bursts Intensity Prediction

Embedded Deep Bilinear Interactive Information and Selective Fusion for Multi-view Learning

no code implementations13 Jul 2020 Jinglin Xu, Wenbin Li, Jiantao Shen, Xinwang Liu, Peicheng Zhou, Xiangsen Zhang, Xiwen Yao, Junwei Han

That is, we seamlessly embed various intra-view information, cross-view multi-dimension bilinear interactive information, and a new view ensemble mechanism into a unified framework to make a decision via the optimization.

Classification General Classification +1

SimpleMKKM: Simple Multiple Kernel K-means

1 code implementation11 May 2020 Xinwang Liu, En Zhu, Jiyuan Liu, Timothy Hospedales, Yang Wang, Meng Wang

We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM).

Clustering

Outlier Detection Ensemble with Embedded Feature Selection

no code implementations15 Jan 2020 Li Cheng, Yijie Wang, Xinwang Liu, Bin Li

Existing methods usually perform feature selection and outlier scoring separately, which would select feature subsets that may not optimally serve for outlier detection, leading to unsatisfying performance.

feature selection Outlier Detection

Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network

1 code implementation NeurIPS 2019 Siqi Wang, Yijie Zeng, Xinwang Liu, En Zhu, Jianping Yin, Chuanfu Xu, Marius Kloft

Despite the wide success of deep neural networks (DNN), little progress has been made on end-to-end unsupervised outlier detection (UOD) from high dimensional data like raw images.

Outlier Detection Representation Learning +1

Understand Dynamic Regret with Switching Cost for Online Decision Making

no code implementations28 Nov 2019 Yawei Zhao, Qian Zhao, Xingxing Zhang, En Zhu, Xinwang Liu, Jianping Yin

We provide a new theoretical analysis framework, which shows an interesting observation, that is, the relation between the switching cost and the dynamic regret is different for settings of OA and OCO.

Decision Making Relation

Simultaneous Clustering and Optimization for Evolving Datasets

no code implementations4 Aug 2019 Yawei Zhao, En Zhu, Xinwang Liu, Chang Tang, Deke Guo, Jianping Yin

Specifically, we propose a new variant of the alternating direction method of multipliers (ADMM) to solve this problem efficiently.

Clustering regression

Dynamic Online Gradient Descent with Improved Query Complexity: A Theoretical Revisit

no code implementations26 Dec 2018 Yawei Zhao, En Zhu, Xinwang Liu, Jianping Yin

We provide a new theoretical analysis framework to investigate online gradient descent in the dynamic environment.

Deep Learning for Generic Object Detection: A Survey

no code implementations6 Sep 2018 Li Liu, Wanli Ouyang, Xiaogang Wang, Paul Fieguth, Jie Chen, Xinwang Liu, Matti Pietikäinen

Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images.

Object object-detection +1

Triangle Lasso for Simultaneous Clustering and Optimization in Graph Datasets

no code implementations20 Aug 2018 Yawei Zhao, Kai Xu, Xinwang Liu, En Zhu, Xinzhong Zhu, Jianping Yin

The reason is that it finds the similar instances according to their features directly, which is usually impacted by the imperfect data, and thus returns sub-optimal results.

Clustering

Cooperative Training of Deep Aggregation Networks for RGB-D Action Recognition

no code implementations5 Dec 2017 Pichao Wang, Wanqing Li, Jun Wan, Philip Ogunbona, Xinwang Liu

Differently from the conventional ConvNet that learns the deep separable features for homogeneous modality-based classification with only one softmax loss function, the c-ConvNet enhances the discriminative power of the deeply learned features and weakens the undesired modality discrepancy by jointly optimizing a ranking loss and a softmax loss for both homogeneous and heterogeneous modalities.

Action Recognition Temporal Action Localization

DeepKSPD: Learning Kernel-matrix-based SPD Representation for Fine-grained Image Recognition

no code implementations ECCV 2018 Melih Engin, Lei Wang, Luping Zhou, Xinwang Liu

Being symmetric positive-definite (SPD), covariance matrix has traditionally been used to represent a set of local descriptors in visual recognition.

Fine-Grained Image Recognition

A Computational Model of the Short-Cut Rule for 2D Shape Decomposition

no code implementations7 Sep 2014 Lei Luo, Chunhua Shen, Xinwang Liu, Chunyuan Zhang

We propose and implement a computational model for the short-cut rule and apply it to the problem of shape decomposition.

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