no code implementations • 30 Oct 2024 • Zichen Wen, Tianyi Wu, Yazhou Ren, Yawen Ling, Chenhang Cui, Xiaorong Pu, Lifang He
It mainly aims to reconstruct the graph structure adapted to traditional GNNs to deal with heterophilous graph issues while maintaining the advantages of traditional GNNs.
1 code implementation • 12 Oct 2024 • Xinyue Chen, Yazhou Ren, Jie Xu, Fangfei Lin, Xiaorong Pu, Yang Yang
To address the client gap, we design a local-synergistic contrastive learning approach that helps single-view clients and multi-view clients achieve consistency for mitigating heterogeneity among all clients.
no code implementations • 4 Oct 2024 • Jianpeng Chen, Yawen Ling, Yazhou Ren, Zichen Wen, Tianyi Wu, Shufei Zhang, Lifang He
With the increasing prevalence of graph-structured data, multi-view graph clustering has been widely used in various downstream applications.
1 code implementation • 14 Mar 2024 • Zhen Long, Qiyuan Wang, Yazhou Ren, Yipeng Liu, Ce Zhu
Specifically, we first construct the embedding feature tensor by stacking the embedding features of different views into a tensor and rotating it.
1 code implementation • 5 Jan 2024 • Zichen Wen, Yawen Ling, Yazhou Ren, Tianyi Wu, Jianpeng Chen, Xiaorong Pu, Zhifeng Hao, Lifang He
Then we design an adaptive hybrid graph filter that is related to the homophily degree, which learns the node embedding based on the graph joint aggregation matrix.
1 code implementation • CVPR 2024 • Zhen Long, Qiyuan Wang, Yazhou Ren, Yipeng Liu, Ce Zhu
Specifically we first construct the embedding feature tensor by stacking the embedding features of different views into a tensor and rotating it.
no code implementations • 24 Sep 2023 • Xinyue Chen, Jie Xu, Yazhou Ren, Xiaorong Pu, Ce Zhu, Xiaofeng Zhu, Zhifeng Hao, Lifang He
Second, the storage and usage of data from multiple clients in a distributed environment can lead to incompleteness of multi-view data.
1 code implementation • 16 May 2023 • Zhen Long, Ce Zhu, Jie Chen, Zihan Li, Yazhou Ren, Yipeng Liu
Benefiting from multiple interactions among orthogonal/semi-orthogonal (low-rank) factors, the low-rank MERA has a strong representation power to capture the complex inter/intra-view information in the self-representation tensor.
1 code implementation • 11 May 2023 • Chenhang Cui, Yazhou Ren, Jingyu Pu, Xiaorong Pu, Lifang He
To significantly reduce the complexity, we construct an anchor graph with small size for each view.
1 code implementation • CVPR 2024 • Jie Xu, Yazhou Ren, Xiaolong Wang, Lei Feng, Zheng Zhang, Gang Niu, Xiaofeng Zhu
Multi-view clustering (MVC) aims at exploring category structures among multi-view data in self-supervised manners.
no code implementations • 21 Mar 2023 • Zhenqian Wu, Xiaoyuan Li, Yazhou Ren, Xiaorong Pu, Xiaofeng Zhu, Lifang He
In order to better learn these neutral expression-disentangled features (NDFs) and to alleviate the non-convex optimization problem, a self-paced learning (SPL) strategy based on NDFs is proposed in the training stage.
no code implementations • 13 Feb 2023 • Song Wu, Yazhou Ren, Aodi Yang, Xinyue Chen, Xiaorong Pu, Jing He, Liqiang Nie, Philip S. Yu
In this survey, we investigate the main contributions of deep learning applications using medical images in fighting against COVID-19 from the aspects of image classification, lesion localization, and severity quantification, and review different deep learning architectures and some image preprocessing techniques for achieving a preciser diagnosis.
no code implementations • ICCV 2023 • Fangfei Lin, Bing Bai, Yiwen Guo, Hao Chen, Yazhou Ren, Zenglin Xu
Multi-view hierarchical clustering (MCHC) plays a pivotal role in comprehending the structures within multi-view data, which hinges on the skillful interaction between hierarchical feature learning and comprehensive representation learning across multiple views.
1 code implementation • 13 Oct 2022 • Jianpeng Chen, Yawen Ling, Jie Xu, Yazhou Ren, Shudong Huang, Xiaorong Pu, Zhifeng Hao, Philip S. Yu, Lifang He
The critical point of MGC is to better utilize view-specific and view-common information in features and graphs of multiple views.
no code implementations • 11 Oct 2022 • Terry Yue Zhuo, Yaqing Liao, Yuecheng Lei, Lizhen Qu, Gerard de Melo, Xiaojun Chang, Yazhou Ren, Zenglin Xu
We introduce ViLPAct, a novel vision-language benchmark for human activity planning.
no code implementations • Proceedings of the 30th ACM International Conference on Multimedia 2022 • Jianjian Shao, Yuanyan Luo, Shudong Huang, Xiaorong Pu, Yazhou Ren
To this end, we propose a play-and-plug method of self-paced label distribution learning (SPLDL) for in-the-wild FER.
Facial Expression Recognition
Facial Expression Recognition (FER)
no code implementations • 9 Oct 2022 • Yazhou Ren, Jingyu Pu, Zhimeng Yang, Jie Xu, Guofeng Li, Xiaorong Pu, Philip S. Yu, Lifang He
Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering.
no code implementations • 8 May 2022 • Zongmo Huang, Yazhou Ren, Xiaorong Pu, Lifang He
To address this issue, in this paper we propose Deep Embedded Multi-view Clustering via Jointly Learning Latent Representations and Graphs (DMVCJ), which utilizes the latent graphs to promote the performance of deep embedded MVC models from two aspects.
no code implementations • 5 May 2022 • Fangfei Lin, Bing Bai, Kun Bai, Yazhou Ren, Peng Zhao, Zenglin Xu
Then, we embed the representations into a hyperbolic space and optimize the hyperbolic embeddings via a continuous relaxation of hierarchical clustering loss.
1 code implementation • 13 Dec 2021 • Lili Pan, Mingming Meng, Yazhou Ren, Yali Zheng, Zenglin Xu
To answer this question, this paper proposes a new SPL method: easy and underrepresented examples first, for learning DDMs.
no code implementations • 9 Sep 2021 • Peng Yi, Kecheng Chen, Zhaoqi Ma, Di Zhao, Xiaorong Pu, Yazhou Ren
To address this issue, we propose a novel 1-D EEG signal denoising network with 2-D transformer, namely EEGDnet.
2 code implementations • 21 Jun 2021 • Jianpeng Chen, Yujing Wang, Ming Zeng, Zongyi Xiang, Bitan Hou, Yunhai Tong, Ole J. Mengshoel, Yazhou Ren
Specifically, the proposed CustomGNN can automatically learn the high-level semantics for specific downstream tasks to highlight semantically relevant paths as well to filter out task-irrelevant noises in a graph.
1 code implementation • CVPR 2022 • Jie Xu, Huayi Tang, Yazhou Ren, Liang Peng, Xiaofeng Zhu, Lifang He
Our method learns different levels of features from the raw features, including low-level features, high-level features, and semantic labels/features in a fusion-free manner, so that it can effectively achieve the reconstruction objective and the consistency objectives in different feature spaces.
no code implementations • ICCV 2021 • Jie Xu, Yazhou Ren, Huayi Tang, Xiaorong Pu, Xiaofeng Zhu, Ming Zeng, Lifang He
The prior of view-common variable obeys approximately discrete Gumbel Softmax distribution, which is introduced to extract the common cluster factor of multiple views.
3 code implementations • 15 May 2021 • Kecheng Chen, Jiayu Sun, Jiang Shen, Jixiang Luo, Xinyu Zhang, Xuelin Pan, Dongsheng Wu, Yue Zhao, Miguel Bento, Yazhou Ren, Xiaorong Pu
To address this issue, we propose a novel graph convolutional network-based LDCT denoising model, namely GCN-MIF, to explicitly perform multi-information fusion for denoising purpose.
no code implementations • 19 Apr 2021 • Zongmo Huang, Yazhou Ren, Xiaorong Pu, Lifang He
In NSMVC, we directly assign different exponents to different views according to their qualities.
no code implementations • 18 Apr 2021 • Kecheng Chen, Kun Long, Yazhou Ren, Jiayu Sun, Xiaorong Pu
To this end, we propose a play-and-plug medical image denoising framework, namely Lesion-Inspired Denoising Network (LIDnet), to collaboratively improve both denoising performance and detection accuracy of denoised medical images.
1 code implementation • 28 Mar 2021 • Jie Xu, Yazhou Ren, Huayi Tang, Zhimeng Yang, Lili Pan, Yang Yang, Xiaorong Pu
To leverage the multi-view complementary information, we concatenate all views' embedded features to form the global features, which can overcome the negative impact of some views' unclear clustering structures.
1 code implementation • 26 Jul 2020 • Jie Xu, Yazhou Ren, Guofeng Li, Lili Pan, Ce Zhu, Zenglin Xu
Firstly, the embedded representations of multiple views are learned individually by deep autoencoders.
no code implementations • ECCV 2020 • Lili Pan, Shijie Ai, Yazhou Ren, Zenglin Xu
Deep discriminative models (e. g. deep regression forests, deep neural decision forests) have achieved remarkable success recently to solve problems such as facial age estimation and head pose estimation.
no code implementations • 8 Oct 2019 • Shijie Ai, Lili Pan, Yazhou Ren
Facial age estimation is an important and challenging problem in computer vision.
no code implementations • 11 May 2019 • Xuanwu Liu, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Yazhou Ren, Maozu Guo
Next, to expand the semantic representation power of hand-crafted features, RDCMH integrates the semantic ranking information into deep cross-modal hashing and jointly optimizes the compatible parameters of deep feature representations and of hashing functions.
1 code implementation • Neurocomputing 2019 • Yazhou Ren, Kangrong Hu, Xinyi Dai, Lili Pan, Steven C. H. Hoi, Zenglin Xu
Deep embedded clustering (DEC) is one of the state-of-the-art deep clustering methods.
no code implementations • 17 Dec 2018 • Lili Pan, Shen Cheng, Jian Liu, Yazhou Ren, Zenglin Xu
We study the problem of multimodal generative modelling of images based on generative adversarial networks (GANs).
1 code implementation • 11 Dec 2018 • Yazhou Ren, Ni Wang, Mingxia Li, Zenglin Xu
Recently, deep clustering, which is able to perform feature learning that favors clustering tasks via deep neural networks, has achieved remarkable performance in image clustering applications.
Ranked #1 on
Image Clustering
on LetterA-J
1 code implementation • 24 Aug 2018 • Yazhou Ren, Xiaofan Que, Dezhong Yao, Zenglin Xu
Despite the success of traditional MTC models, they are either easy to stuck into local optima, or sensitive to outliers and noisy data.