no code implementations • 21 Apr 2024 • Jiaxin Zhang, Yiqi Wang, Xihong Yang, Siwei Wang, Yu Feng, Yu Shi, Ruicaho Ren, En Zhu, Xinwang Liu
Graph Neural Networks have demonstrated great success in various fields of multimedia.
no code implementations • 20 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.
no code implementations • 14 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.
no code implementations • 13 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.
no code implementations • 7 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.
no code implementations • 2 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.
1 code implementation • 11 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.
1 code implementation • 28 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.
no code implementations • 28 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.
1 code implementation • 16 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.
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.
no code implementations • 23 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.
no code implementations • 26 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.
1 code implementation • 21 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.
1 code implementation • 12 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.
1 code implementation • 31 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.
1 code implementation • 31 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).
1 code implementation • 17 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.
2 code implementations • 17 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).
2 code implementations • 13 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).
1 code implementation • 7 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.
no code implementations • 6 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.
1 code implementation • 26 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.
1 code implementation • 23 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.
1 code implementation • 14 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.
no code implementations • 8 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).
no code implementations • 8 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.
no code implementations • 8 Jun 2023 • Meng Liu, Ke Liang, Yue Liu, Siwei Wang, Sihang Zhou, Xinwang Liu
It makes evaluating models for large-scale temporal graph clustering challenging.
no code implementations • 4 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.
3 code implementations • 28 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.
no code implementations • 23 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.
1 code implementation • 18 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.
no code implementations • 20 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.
no code implementations • 4 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.
no code implementations • CVPR 2023 • Jiaqi Jin, Siwei Wang, Zhibin Dong, Xinwang Liu, En Zhu
The success of existing multi-view clustering relies on the assumption of sample integrity across multiple views.
no code implementations • 14 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.
no code implementations • 15 Feb 2023 • Meng Liu, Ke Liang, Yawei Zhao, Wenxuan Tu, Sihang Zhou, Xinwang Liu, Kunlun He
To solve this issue, by extracting both temporal and structural information to learn more informative node representations, we propose a self-supervised method termed S2T for temporal graph learning.
no code implementations • 15 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.
1 code implementation • 21 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.
1 code implementation • 3 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.
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.
2 code implementations • 16 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.
1 code implementation • 12 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.
1 code implementation • 7 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.
no code implementations • 28 Nov 2022 • Jingcan Duan, Bin Xiao, Siwei Wang, Haifang Zhou, Xinwang Liu
The average node-pair similarity can be regarded as the topology anomaly degree of nodes within substructures.
2 code implementations • 23 Nov 2022 • Yue Liu, Jun Xia, Sihang Zhou, Xihong Yang, Ke Liang, Chenchen Fan, Yan Zhuang, Stan Z. Li, Xinwang Liu, Kunlun He
However, the corresponding survey paper is relatively scarce, and it is imminent to make a summary of this field.
no code implementations • 19 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.
1 code implementation • 2 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.
no code implementations • 13 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.
1 code implementation • 5 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.
no code implementations • 6 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.
1 code implementation • 30 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.
no code implementations • 11 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.
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.
no code implementations • 25 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.
no code implementations • 24 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.
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.
1 code implementation • CVPR 2022 • Siwei Wang, Xinwang Liu, Li Liu, Wenxuan Tu, Xinzhong Zhu, Jiyuan Liu, Sihang Zhou, En Zhu
Multi-view clustering has received increasing attention due to its effectiveness in fusing complementary information without manual annotations.
2 code implementations • 29 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.
no code implementations • 9 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.
1 code implementation • 5 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.
no code implementations • CVPR 2022 • Guang Yu, Siqi Wang, Zhiping Cai, Xinwang Liu, Chuanfu Xu, Chengkun Wu
With this property, we propose Localization based Reconstruction (LBR) as a strong UVAD baseline and a solid foundation of our solution.
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.
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.
no code implementations • 1 May 2021 • Chen Zhang, Siwei Wang, Jiyuan Liu, Sihang Zhou, Pei Zhang, Xinwang Liu, En Zhu, Changwang Zhang
iii) The partition level information has not been utilized in existing work.
no code implementations • 1 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.
no code implementations • 27 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.
1 code implementation • International Joint Conferences on Artificial Intelligence Organization 2021 • Chang Tang, Xinwang Liu, En Zhu, Lizhe Wang, Albert Zomaya
In this paper, we propose a hyperspectral band selection method via spatial-spectral weighted region-wise multiple graph fusion-based spectral clustering, referred to as RMGF briefly.
no code implementations • 21 Mar 2021 • Mingjie Luo, Siwei Wang, Xinwang Liu, Wenxuan Tu, Yi Zhang, Xifeng Guo, Sihang Zhou, En Zhu
Clustering is a fundamental task in the computer vision and machine learning community.
no code implementations • 10 Mar 2021 • Xiang Wang, Xiaoyong Li, Junxing Zhu, Zichen Xu, Kaijun Ren, Weiming Zhang, Xinwang Liu, Kui Yu
Real-world data usually have high dimensionality and it is important to mitigate the curse of dimensionality.
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.
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.
1 code implementation • 15 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.
1 code implementation • 5 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.
no code implementations • 31 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.
no code implementations • 31 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.
no code implementations • 13 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.
1 code implementation • 11 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).
no code implementations • 15 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.
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.
no code implementations • 28 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.
no code implementations • 4 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.
no code implementations • 26 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.
no code implementations • 6 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.
no code implementations • 20 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.
no code implementations • 5 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.
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.
no code implementations • 7 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.