Search Results for author: Ce Zhu

Found 59 papers, 24 papers with code

TERM Model: Tensor Ring Mixture Model for Density Estimation

no code implementations13 Dec 2023 Ruituo Wu, Jiani Liu, Ce Zhu, Anh-Huy Phan, Ivan V. Oseledets, Yipeng Liu

However, a substantial number of potential tensor permutations can lead to a tensor network with the same structure but varying expressive capabilities.

Density Estimation Ensemble Learning

A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using dMRI Tractography

no code implementations25 Nov 2023 Haolin He, Ce Zhu, Le Zhang, Yipeng Liu, Xiao Xu, Yuqian Chen, Leo Zekelman, Jarrett Rushmore, Yogesh Rathi, Nikos Makris, Lauren J. O'Donnell, Fan Zhang

The amygdala plays a vital role in emotional processing and exhibits structural diversity that necessitates fine-scale parcellation for a comprehensive understanding of its anatomico-functional correlations.

Clustering Deep Clustering +1

Phase Guided Light Field for Spatial-Depth High Resolution 3D Imaging

no code implementations17 Nov 2023 Geyou Zhang, Ce Zhu, Kai Liu, Yipeng Liu

On 3D imaging, light field cameras typically are of single shot, and however, they heavily suffer from low spatial resolution and depth accuracy.

Stereo Matching

Federated Deep Multi-View Clustering with Global Self-Supervision

no code implementations24 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.


Low-Rank Multitask Learning based on Tensorized SVMs and LSSVMs

1 code implementation30 Aug 2023 Jiani Liu, Qinghua Tao, Ce Zhu, Yipeng Liu, Xiaolin Huang, Johan A. K. Suykens

In contrast to previous MTL frameworks, our decision function in the dual induces a weighted kernel function with a task-coupling term characterized by the similarities of the task-specific factors, better revealing the explicit relations across tasks in MTL.

Tensor Regression

13 code implementations22 Aug 2023 Jiani Liu, Ce Zhu, Zhen Long, Yipeng Liu

Tensors, as high dimensional extensions of vectors, are considered as natural representations of high dimensional data.


Feature Modulation Transformer: Cross-Refinement of Global Representation via High-Frequency Prior for Image Super-Resolution

1 code implementation ICCV 2023 Ao Li, Le Zhang, Yun Liu, Ce Zhu

Transformer-based methods have exhibited remarkable potential in single image super-resolution (SISR) by effectively extracting long-range dependencies.

Image Super-Resolution

Multi-view MERA Subspace Clustering

1 code implementation16 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.

Clustering Multi-view Subspace Clustering

Illumination-insensitive Binary Descriptor for Visual Measurement Based on Local Inter-patch Invariance

1 code implementation13 May 2023 Xinyu Lin, Yingjie Zhou, Xun Zhang, Yipeng Liu, Ce Zhu

Existing binary descriptors may not perform well for long-term visual measurement tasks due to their sensitivity to illumination variations.

Semantic Segmentation Visual Localization

Adaptively Topological Tensor Network for Multi-view Subspace Clustering

no code implementations1 May 2023 Yipeng Liu, Yingcong Lu, Weiting Ou, Zhen Long, Ce Zhu

Therefore, a pre-defined tensor decomposition may not fully exploit low rank information for a certain dataset, resulting in sub-optimal multi-view clustering performance.

Clustering Multi-view Subspace Clustering +2

A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges

no code implementations29 Apr 2023 Xinyu Lin, Yingjie Zhou, Yipeng Liu, Ce Zhu

The challenges in existing methods and corresponding insights for potentially solving them are also provided to inspire researchers.

Line Segment Detection

Low Rank Optimization for Efficient Deep Learning: Making A Balance between Compact Architecture and Fast Training

no code implementations22 Mar 2023 Xinwei Ou, Zhangxin Chen, Ce Zhu, Yipeng Liu

However, the high computational complexity and storage cost makes deep learning hard to be used on resource-constrained devices, and it is not environmental-friendly with much power cost.

Model Compression Quantization

Tensorized LSSVMs for Multitask Regression

no code implementations4 Mar 2023 Jiani Liu, Qinghua Tao, Ce Zhu, Yipeng Liu, Johan A. K. Suykens

Multitask learning (MTL) can utilize the relatedness between multiple tasks for performance improvement.


FedTADBench: Federated Time-Series Anomaly Detection Benchmark

1 code implementation19 Dec 2022 Fanxing Liu, Cheng Zeng, Le Zhang, Yingjie Zhou, Qing Mu, Yanru Zhang, Ling Zhang, Ce Zhu

We would like to answer the following questions: (1)How is the performance of time series anomaly detection algorithms when meeting federated learning?

Anomaly Detection Federated Learning +2

Deep Negative Correlation Classification

no code implementations14 Dec 2022 Le Zhang, Qibin Hou, Yun Liu, Jia-Wang Bian, Xun Xu, Joey Tianyi Zhou, Ce Zhu

Ensemble learning serves as a straightforward way to improve the performance of almost any machine learning algorithm.

Classification Ensemble Learning

Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report

2 code implementations7 Nov 2022 Andrey Ignatov, Radu Timofte, Maurizio Denna, Abdel Younes, Ganzorig Gankhuyag, Jingang Huh, Myeong Kyun Kim, Kihwan Yoon, Hyeon-Cheol Moon, Seungho Lee, Yoonsik Choe, Jinwoo Jeong, Sungjei Kim, Maciej Smyl, Tomasz Latkowski, Pawel Kubik, Michal Sokolski, Yujie Ma, Jiahao Chao, Zhou Zhou, Hongfan Gao, Zhengfeng Yang, Zhenbing Zeng, Zhengyang Zhuge, Chenghua Li, Dan Zhu, Mengdi Sun, Ran Duan, Yan Gao, Lingshun Kong, Long Sun, Xiang Li, Xingdong Zhang, Jiawei Zhang, Yaqi Wu, Jinshan Pan, Gaocheng Yu, Jin Zhang, Feng Zhang, Zhe Ma, Hongbin Wang, Hojin Cho, Steve Kim, Huaen Li, Yanbo Ma, Ziwei Luo, Youwei Li, Lei Yu, Zhihong Wen, Qi Wu, Haoqiang Fan, Shuaicheng Liu, Lize Zhang, Zhikai Zong, Jeremy Kwon, Junxi Zhang, Mengyuan Li, Nianxiang Fu, Guanchen Ding, Han Zhu, Zhenzhong Chen, Gen Li, Yuanfan Zhang, Lei Sun, Dafeng Zhang, Neo Yang, Fitz Liu, Jerry Zhao, Mustafa Ayazoglu, Bahri Batuhan Bilecen, Shota Hirose, Kasidis Arunruangsirilert, Luo Ao, Ho Chun Leung, Andrew Wei, Jie Liu, Qiang Liu, Dahai Yu, Ao Li, Lei Luo, Ce Zhu, Seongmin Hong, Dongwon Park, Joonhee Lee, Byeong Hyun Lee, Seunggyu Lee, Se Young Chun, Ruiyuan He, Xuhao Jiang, Haihang Ruan, Xinjian Zhang, Jing Liu, Garas Gendy, Nabil Sabor, Jingchao Hou, Guanghui He

While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.

Image Super-Resolution

Tucker-O-Minus Decomposition for Multi-view Tensor Subspace Clustering

no code implementations23 Oct 2022 Yingcong Lu, Yipeng Liu, Zhen Long, Zhangxin Chen, Ce Zhu

To alleviate these problems, we propose a new tensor decomposition called Tucker-O-Minus Decomposition (TOMD) for multi-view clustering.

Clustering Tensor Decomposition

Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing

1 code implementation17 Mar 2022 Zhiyuan Zha, Bihan Wen, Xin Yuan, Saiprasad Ravishankar, Jiantao Zhou, Ce Zhu

Furthermore, we present a unified framework for incorporating various GSR and LR models and discuss the relationship between GSR and LR models.

Compressive Sensing

Semi-tensor Product-based TensorDecomposition for Neural Network Compression

no code implementations30 Sep 2021 Hengling Zhao, Yipeng Liu, Xiaolin Huang, Ce Zhu

Tucker decomposition, Tensor Train (TT) and Tensor Ring (TR) are common decomposition for low rank compression of deep neural networks.

Low-rank compression Neural Network Compression +1

Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection

2 code implementations22 May 2021 Yingjie Zhou, Xucheng Song, Yanru Zhang, Fanxing Liu, Ce Zhu, Lingqiao Liu

Weakly-supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data.

Supervised Anomaly Detection Weakly-supervised Anomaly Detection

Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions

no code implementations22 Apr 2021 Jing Wu, Mingyi Zhou, Ce Zhu, Yipeng Liu, Mehrtash Harandi, Li Li

Recently, adversarial attack methods have been developed to challenge the robustness of machine learning models.

Adversarial Attack

Real-World Single Image Super-Resolution: A Brief Review

1 code implementation3 Mar 2021 Honggang Chen, Xiaohai He, Linbo Qing, Yuanyuan Wu, Chao Ren, Ce Zhu

More specifically, this review covers the critical publically available datasets and assessment metrics for RSISR, and four major categories of RSISR methods, namely the degradation modeling-based RSISR, image pairs-based RSISR, domain translation-based RSISR, and self-learning-based RSISR.

Computational Efficiency Image Super-Resolution +2

Scalable Deep Compressive Sensing

no code implementations20 Jan 2021 Zhonghao Zhang, Yipeng Liu, Xingyu Cao, Fei Wen, Ce Zhu

In this paper, we develop a general framework named scalable deep compressive sensing (SDCS) for the scalable sampling and reconstruction (SSR) of all existing end-to-end-trained models.

Compressive Sensing

Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration

1 code implementation29 Oct 2020 Feng Li, Runmin Cong, Huihui Bai, Yifan He, Yao Zhao, Ce Zhu

In this paper, we present a deep interleaved network (DIN) that learns how information at different states should be combined for high-quality (HQ) images reconstruction.

Deblurring Image Deblurring +2

Decision-based Universal Adversarial Attack

1 code implementation15 Sep 2020 Jing Wu, Mingyi Zhou, Shuaicheng Liu, Yipeng Liu, Ce Zhu

A single perturbation can pose the most natural images to be misclassified by classifiers.

Adversarial Attack

Siamese Network for RGB-D Salient Object Detection and Beyond

2 code implementations26 Aug 2020 Keren Fu, Deng-Ping Fan, Ge-Peng Ji, Qijun Zhao, Jianbing Shen, Ce Zhu

Inspired by the observation that RGB and depth modalities actually present certain commonality in distinguishing salient objects, a novel joint learning and densely cooperative fusion (JL-DCF) architecture is designed to learn from both RGB and depth inputs through a shared network backbone, known as the Siamese architecture.

object-detection RGB-D Salient Object Detection +2

Deep Embedded Multi-view Clustering with Collaborative Training

1 code implementation26 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.


Bayesian Low Rank Tensor Ring Model for Image Completion

no code implementations29 Jun 2020 Zhen Long, Ce Zhu, Jiani Liu, Yipeng Liu

Low rank tensor ring model is powerful for image completion which recovers missing entries in data acquisition and transformation.

Bayesian Inference

Unsupervised Feature Selection via Multi-step Markov Transition Probability

no code implementations29 May 2020 Yan Min, Mao Ye, Liang Tian, Yulin Jian, Ce Zhu, Shangming Yang

Our main contributions are a novel feature section approach which uses multi-step transition probability to characterize the data structure, and three algorithms proposed from the positive and negative aspects for keeping data structure.

Dimensionality Reduction feature selection +1

Disentanglement Then Reconstruction: Learning Compact Features for Unsupervised Domain Adaptation

no code implementations28 May 2020 Lihua Zhou, Mao Ye, Xinpeng Li, Ce Zhu, Yiguang Liu, Xue Li

By this reconstructor, we can construct prototypes for the original features using class prototypes and domain prototypes correspondingly.

Disentanglement Unsupervised Domain Adaptation

Learning Various Length Dependence by Dual Recurrent Neural Networks

no code implementations28 May 2020 Chenpeng Zhang, Shuai Li, Mao Ye, Ce Zhu, Xue Li

Many variants of RNN have been proposed to solve the gradient problems of training RNNs and process long sequences.

The Power of Triply Complementary Priors for Image Compressive Sensing

no code implementations16 May 2020 Zhiyuan Zha, Xin Yuan, Joey Tianyi Zhou, Jiantao Zhou, Bihan Wen, Ce Zhu

In this paper, we propose a joint low-rank and deep (LRD) image model, which contains a pair of triply complementary priors, namely \textit{external} and \textit{internal}, \textit{deep} and \textit{shallow}, and \textit{local} and \textit{non-local} priors.

Compressive Sensing Image Restoration

ProbaNet: Proposal-balanced Network for Object Detection

no code implementations6 May 2020 Jing Wu, Xiang Zhang, Mingyi Zhou, Ce Zhu

Candidate object proposals generated by object detectors based on convolutional neural network (CNN) encounter easy-hard samples imbalance problem, which can affect overall performance.

Object object-detection +1

AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensing

1 code implementation21 Apr 2020 Zhonghao Zhang, Yipeng Liu, Jiani Liu, Fei Wen, Ce Zhu

By unfolding the iterative optimization algorithm for model-based methods onto networks, deep unfolding methods have the good interpretation of model-based methods and the high speed of classical deep network methods.

Blocking Compressive Sensing +1

Frequency-Weighted Robust Tensor Principal Component Analysis

no code implementations21 Apr 2020 Shenghan Wang, Yipeng Liu, Lanlan Feng, Ce Zhu

The newly obtained frequency-weighted RTPCA can be solved by alternating direction method of multipliers, and it is the first time that frequency analysis is taken in tensor principal component analysis.

Color Image Denoising Image Denoising

DaST: Data-free Substitute Training for Adversarial Attacks

2 code implementations CVPR 2020 Mingyi Zhou, Jing Wu, Yipeng Liu, Shuaicheng Liu, Ce Zhu

In this paper, we propose a data-free substitute training method (DaST) to obtain substitute models for adversarial black-box attacks without the requirement of any real data.

BIG-bench Machine Learning

Adversarial Imitation Attack

no code implementations28 Mar 2020 Mingyi Zhou, Jing Wu, Yipeng Liu, Xiaolin Huang, Shuaicheng Liu, Xiang Zhang, Ce Zhu

Then, the adversarial examples generated by the imitation model are utilized to fool the attacked model.

Adversarial Attack

A Unified Framework for Coupled Tensor Completion

no code implementations9 Jan 2020 Huyan Huang, Yipeng Liu, Ce Zhu

To let coupled tensors help each other for missing component estimation, in this paper we utilize TR for coupled completion by sharing parts of the latent factors.

Tensor Decomposition

Distribution-Aware Coordinate Representation for Human Pose Estimation

6 code implementations CVPR 2020 Feng Zhang, Xiatian Zhu, Hanbin Dai, Mao Ye, Ce Zhu

Interestingly, we found that the process of decoding the predicted heatmaps into the final joint coordinates in the original image space is surprisingly significant for human pose estimation performance, which nevertheless was not recognised before.

Ranked #2 on Multi-Person Pose Estimation on MS COCO (using extra training data)

Keypoint Detection Multi-Person Pose Estimation

C3AE: Exploring the Limits of Compact Model for Age Estimation

1 code implementation CVPR 2019 Chao Zhang, Shuaicheng Liu, Xun Xu, Ce Zhu

Recently, MobileNets and ShuffleNets have been proposed to reduce the number of parameters, yielding lightweight models.

Age Estimation

Robust Low-Rank Tensor Ring Completion

no code implementations31 Mar 2019 Huyan Huang, Yipeng Liu, Ce Zhu

To further deal with its sensitivity to sparse component as it does in tensor principle component analysis, we propose robust tensor ring completion (RTRC), which separates latent low-rank tensor component from sparse component with limited number of measurements.

Shadow Removal

Low-rank Tensor Grid for Image Completion

no code implementations12 Mar 2019 Huyan Huang, Yipeng Liu, Ce Zhu

The recently proposed methods based on tensor train (TT) and tensor ring (TR) show better performance in image recovery than classical ones.

Computational Efficiency

Provable Tensor Ring Completion

1 code implementation8 Mar 2019 Huyan Huang, Yipeng Liu, Ce Zhu

Tensor completion recovers a multi-dimensional array from a limited number of measurements.

Matrix Completion

From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration

1 code implementation6 Jul 2018 Zhiyuan Zha, Xin Yuan, Bihan Wen, Jiantao Zhou, Jiachao Zhang, Ce Zhu

Towards this end, we first obtain a good reference of the original image groups by using the image NSS prior, and then the rank residual of the image groups between this reference and the degraded image is minimized to achieve a better estimate to the desired image.

Image Compression Image Denoising +1

Image Ordinal Classification and Understanding: Grid Dropout with Masking Label

no code implementations8 May 2018 Chao Zhang, Ce Zhu, Jimin Xiao, Xun Xu, Yipeng Liu

Finally we demonstrate the effectiveness of both approaches by visualizing the Class Activation Map (CAM) and discover that grid dropout is more aware of the whole facial areas and more robust than neuron dropout for small training dataset.

Age Estimation Classification +3

Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN

11 code implementations CVPR 2018 Shuai Li, Wanqing Li, Chris Cook, Ce Zhu, Yanbo Gao

Experimental results have shown that the proposed IndRNN is able to process very long sequences (over 5000 time steps), can be used to construct very deep networks (21 layers used in the experiment) and still be trained robustly.

Language Modelling Sequential Image Classification +1

Hole Filling with Multiple Reference Views in DIBR View Synthesis

no code implementations8 Feb 2018 Shuai Li, Ce Zhu, Ming-Ting Sun

In this paper, we first examine the view interpolation with multiple reference views, demonstrating that the problem of emerging holes in a target virtual view can be greatly alleviated by making good use of other neighboring complementary views in addition to its two (commonly used) most neighboring primary views.

A Fully Trainable Network with RNN-based Pooling

no code implementations16 Jun 2017 Shuai Li, Wanqing Li, Chris Cook, Ce Zhu, Yanbo Gao

Such a network with learnable pooling function is referred to as a fully trainable network (FTN).

Towards thinner convolutional neural networks through Gradually Global Pruning

no code implementations29 Mar 2017 Zhengtao Wang, Ce Zhu, Zhiqiang Xia, Qi Guo, Yipeng Liu

Deep network pruning is an effective method to reduce the storage and computation cost of deep neural networks when applying them to resource-limited devices.

Network Pruning

Attribute-controlled face photo synthesis from simple line drawing

no code implementations9 Feb 2017 Qi Guo, Ce Zhu, Zhiqiang Xia, Zhengtao Wang, Yipeng Liu

In this paper, we propose a deep generative model to synthesize face photo from simple line drawing controlled by face attributes such as hair color and complexion.


Iterative Block Tensor Singular Value Thresholding for Extraction of Low Rank Component of Image Data

no code implementations15 Jan 2017 Longxi Chen, Yipeng Liu, Ce Zhu

In this paper, we propose a new robust TPCA method to extract the princi- pal components of the multi-way data based on tensor singular value decomposition.

A Comparative Study for the Nuclear Norms Minimization Methods

no code implementations16 Aug 2016 Zhiyuan Zha, Bihan Wen, Jiachao Zhang, Jiantao Zhou, Ce Zhu

Inspired by enhancing sparsity of the weighted L1-norm minimization in comparison with L1-norm minimization in sparse representation, we thus explain that WNNM is more effective than NMM.

Deblurring Dictionary Learning +2

Every Filter Extracts A Specific Texture In Convolutional Neural Networks

1 code implementation15 Aug 2016 Zhiqiang Xia, Ce Zhu, Zhengtao Wang, Qi Guo, Yipeng Liu

We also demonstrate that style of images could be a combination of these texture primitives.

3D Keypoint Detection Based on Deep Neural Network with Sparse Autoencoder

no code implementations30 Apr 2016 Xinyu Lin, Ce Zhu, Qian Zhang, Yipeng Liu

Researchers have proposed various methods to extract 3D keypoints from the surface of 3D mesh models over the last decades, but most of them are based on geometric methods, which lack enough flexibility to meet the requirements for various applications.

Keypoint Detection regression

Mesh Interest Point Detection Based on Geometric Measures and Sparse Refinement

no code implementations29 Apr 2016 Xinyu Lin, Ce Zhu, Yipeng Liu

Three dimensional (3D) interest point detection plays a fundamental role in 3D computer vision and graphics.

Interest Point Detection

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