Search Results for author: Zhen Cui

Found 37 papers, 3 papers with code

Graph Jigsaw Learning for Cartoon Face Recognition

no code implementations14 Jul 2021 Yong Li, Lingjie Lao, Zhen Cui, Shiguang Shan, Jian Yang

To mitigate this issue, we propose the GraphJigsaw that constructs jigsaw puzzles at various stages in the classification network and solves the puzzles with the graph convolutional network (GCN) in a progressive manner.

Face Recognition

Consistent Instance False Positive Improves Fairness in Face Recognition

1 code implementation CVPR 2021 Xingkun Xu, Yuge Huang, Pengcheng Shen, Shaoxin Li, Jilin Li, Feiyue Huang, Yong Li, Zhen Cui

Then, an additional penalty term, which is in proportion to the ratio of instance FPR overall FPR, is introduced into the denominator of the softmax-based loss.

Face Recognition Fairness

Global Information Guided Video Anomaly Detection

no code implementations14 Apr 2021 Hui Lv, Chunyan Xu, Zhen Cui

Video anomaly detection (VAD) is currently a challenging task due to the complexity of anomaly as well as the lack of labor-intensive temporal annotations.

Anomaly Detection

Learning Normal Dynamics in Videos with Meta Prototype Network

1 code implementation CVPR 2021 Hui Lv, Chen Chen, Zhen Cui, Chunyan Xu, Yong Li, Jian Yang

Frame reconstruction (current or future frame) based on Auto-Encoder (AE) is a popular method for video anomaly detection.

Anomaly Detection Meta-Learning

Spatial-Temporal Tensor Graph Convolutional Network for Traffic Prediction

no code implementations10 Mar 2021 Xuran Xu, Tong Zhang, Chunyan Xu, Zhen Cui, Jian Yang

We further extend graph convolution into tensor space and propose a tensor graph convolution network to extract more discriminating features from spatial-temporal graph data.

Tensor Decomposition Traffic Prediction

Graph Deformer Network

no code implementations1 Jan 2021 Wenting Zhao, Yuan Fang, Zhen Cui, Tong Zhang, Jian Yang, Wei Liu

In this paper, we propose a simple yet effective graph deformer network (GDN) to fulfill anisotropic convolution filtering on graphs, analogous to the standard convolution operation on images.

Interest-Behaviour Multiplicative Network for Resource-limited Recommendation

no code implementations24 Sep 2020 Qianliang Wu, Tong Zhang, Zhen Cui, Jian Yang

In this paper, we aim to mine the cue of user preferences in resource-limited recommendation tasks, for which purpose we specifically build a large used car transaction dataset possessing resource-limitation characteristics.

Spatial Transformer Point Convolution

no code implementations3 Sep 2020 Yuan Fang, Chunyan Xu, Zhen Cui, Yuan Zong, Jian Yang

In this paper, we propose a spatial transformer point convolution (STPC) method to achieve anisotropic convolution filtering on point clouds.

Dictionary Learning Semantic Segmentation

Localizing Anomalies from Weakly-Labeled Videos

no code implementations20 Aug 2020 Hui Lv, Chuanwei Zhou, Chunyan Xu, Zhen Cui, Jian Yang

In addition, in order to fully utilize the spatial context information, the immediate semantics are directly derived from the segment representations.

Anomaly Detection

Instance-Aware Graph Convolutional Network for Multi-Label Classification

no code implementations19 Aug 2020 Yun Wang, Tong Zhang, Zhen Cui, Chunyan Xu, Jian Yang

For label diffusion of instance-awareness in graph convolution, rather than using the statistical label correlation alone, an image-dependent label correlation matrix (LCM), fusing both the statistical LCM and an individual one of each image instance, is constructed for graph inference on labels to inject adaptive information of label-awareness into the learned features of the model.

General Classification Multi-Label Classification +1

Graph Wasserstein Correlation Analysis for Movie Retrieval

no code implementations ECCV 2020 Xueya Zhang, Tong Zhang, Xiaobin Hong, Zhen Cui, Jian Yang

Spectral graph filtering is introduced to encode graph signals, which are then embedded as probability distributions in a Wasserstein space, called graph Wasserstein metric learning.

Metric Learning

Graph Inference Learning for Semi-supervised Classification

no code implementations ICLR 2020 Chunyan Xu, Zhen Cui, Xiaobin Hong, Tong Zhang, Jian Yang, Wei Liu

In this work, we address semi-supervised classification of graph data, where the categories of those unlabeled nodes are inferred from labeled nodes as well as graph structures.

General Classification Node Classification

Dual-Attention Graph Convolutional Network

no code implementations28 Nov 2019 Xueya Zhang, Tong Zhang, Wenting Zhao, Zhen Cui, Jian Yang

Graph convolutional networks (GCNs) have shown the powerful ability in text structure representation and effectively facilitate the task of text classification.

Text Classification

Pose Neural Fabrics Search

2 code implementations16 Sep 2019 Sen Yang, Wankou Yang, Zhen Cui

Neural Architecture Search (NAS) technologies have emerged in many domains to jointly learn the architectures and weights of the neural network.

Image Classification Keypoint Detection +3

Cross-Database Micro-Expression Recognition: A Benchmark

no code implementations19 Dec 2018 Yuan Zong, Tong Zhang, Wenming Zheng, Xiaopeng Hong, Chuangao Tang, Zhen Cui, Guoying Zhao

Cross-database micro-expression recognition (CDMER) is one of recently emerging and interesting problem in micro-expression analysis.

Domain Adaptation Micro-Expression Recognition +1

Cross-database non-frontal facial expression recognition based on transductive deep transfer learning

no code implementations30 Nov 2018 Keyu Yan, Wenming Zheng, Tong Zhang, Yuan Zong, Zhen Cui

Cross-database non-frontal expression recognition is a very meaningful but rather difficult subject in the fields of computer vision and affect computing.

Facial Expression Recognition Transfer Learning

Gaussian-Induced Convolution for Graphs

no code implementations11 Nov 2018 Jiatao Jiang, Zhen Cui, Chunyan Xu, Jian Yang

In this work, we propose a Gaussian-induced convolution (GIC) framework to conduct local convolution filtering on irregular graphs.

Graph Classification Learning Representation On Graph

Context-Dependent Diffusion Network for Visual Relationship Detection

no code implementations11 Sep 2018 Zhen Cui, Chunyan Xu, Wenming Zheng, Jian Yang

Visual relationship detection can bridge the gap between computer vision and natural language for scene understanding of images.

Object Recognition Scene Understanding +1

Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation

no code implementations ECCV 2018 Zhen-Yu Zhang, Zhen Cui, Chunyan Xu, Zequn Jie, Xiang Li, Jian Yang

In this paper, we propose a novel joint Task-Recursive Learning (TRL) framework for the closing-loop semantic segmentation and monocular depth estimation tasks.

Monocular Depth Estimation Semantic Segmentation

When Work Matters: Transforming Classical Network Structures to Graph CNN

no code implementations7 Jul 2018 Wenting Zhao, Chunyan Xu, Zhen Cui, Tong Zhang, Jiatao Jiang, Zhen-Yu Zhang, Jian Yang

In this paper, we aim to give a comprehensive analysis of when work matters by transforming different classical network structures to graph CNN, particularly in the basic graph recognition problem.

Graph Classification Video Understanding

Walk-Steered Convolution for Graph Classification

no code implementations16 Apr 2018 Jiatao Jiang, Chunyan Xu, Zhen Cui, Tong Zhang, Wenming Zheng, Jian Yang

As an analogy to a standard convolution kernel on image, Gaussian models implicitly coordinate those unordered vertices/nodes and edges in a local receptive field after projecting to the gradient space of Gaussian parameters.

General Classification Graph Classification +1

Tensor graph convolutional neural network

no code implementations27 Mar 2018 Tong Zhang, Wenming Zheng, Zhen Cui, Yang Li

For cross graph convolution, a parameterized Kronecker sum operation is proposed to generate a conjunctive adjacency matrix characterizing the relationship between every pair of nodes across two subgraphs.

Matrix Completion

Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition

no code implementations27 Feb 2018 Chaolong Li, Zhen Cui, Wenming Zheng, Chunyan Xu, Jian Yang

To encode dynamic graphs, the constructed multi-scale local graph convolution filters, consisting of matrices of local receptive fields and signal mappings, are recursively performed on structured graph data of temporal and spatial domain.

Action Recognition Skeleton Based Action Recognition

Action-Attending Graphic Neural Network

no code implementations17 Nov 2017 Chaolong Li, Zhen Cui, Wenming Zheng, Chunyan Xu, Rongrong Ji, Jian Yang

The motion analysis of human skeletons is crucial for human action recognition, which is one of the most active topics in computer vision.

Action Recognition Skeleton Based Action Recognition

Learning a Target Sample Re-Generator for Cross-Database Micro-Expression Recognition

no code implementations26 Jul 2017 Yuan Zong, Xiaohua Huang, Wenming Zheng, Zhen Cui, Guoying Zhao

In this paper, we investigate the cross-database micro-expression recognition problem, where the training and testing samples are from two different micro-expression databases.

Emotion Recognition Micro-Expression Recognition

Spectral Filter Tracking

no code implementations18 Jul 2017 Zhen Cui, You yi Cai, Wen ming Zheng, Jian Yang

Visual object tracking is a challenging computer vision task with numerous real-world applications.

Graph Matching Visual Object Tracking

Deep manifold-to-manifold transforming network for action recognition

no code implementations30 May 2017 Tong Zhang, Wenming Zheng, Zhen Cui, Chaolong Li

Symmetric positive definite (SPD) matrices (e. g., covariances, graph Laplacians, etc.)

Action Recognition

Spatial-Temporal Recurrent Neural Network for Emotion Recognition

no code implementations12 May 2017 Tong Zhang, Wenming Zheng, Zhen Cui, Yuan Zong, Yang Li

Then a bi-directional temporal RNN layer is further used to learn discriminative temporal dependencies from the sequences concatenating spatial features of each time slice produced from the spatial RNN layer.

EEG Emotion Recognition

Recurrent Regression for Face Recognition

no code implementations24 Jul 2016 Yang Li, Wenming Zheng, Zhen Cui

To address the sequential changes of images including poses, in this paper we propose a recurrent regression neural network(RRNN) framework to unify two classic tasks of cross-pose face recognition on still images and video-based face recognition.

Face Recognition

Recurrently Target-Attending Tracking

no code implementations CVPR 2016 Zhen Cui, Shengtao Xiao, Jiashi Feng, Shuicheng Yan

The produced confidence maps from the RNNs are employed to adaptively regularize the learning of discriminative correlation filters by suppressing clutter background noises while making full use of the information from reliable parts.

Visual Tracking

Recurrent Face Aging

no code implementations CVPR 2016 Wei Wang, Zhen Cui, Yan Yan, Jiashi Feng, Shuicheng Yan, Xiangbo Shu, Nicu Sebe

Modeling the aging process of human face is important for cross-age face verification and recognition.

Face Verification

Deep Recurrent Regression for Facial Landmark Detection

no code implementations30 Oct 2015 Hanjiang Lai, Shengtao Xiao, Yan Pan, Zhen Cui, Jiashi Feng, Chunyan Xu, Jian Yin, Shuicheng Yan

We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent network structures.

Facial Landmark Detection

Generalized Unsupervised Manifold Alignment

no code implementations NeurIPS 2014 Zhen Cui, Hong Chang, Shiguang Shan, Xilin Chen

In this paper, we propose a generalized Unsupervised Manifold Alignment (GUMA) method to build the connections between different but correlated datasets without any known correspondences.

Deeply Coupled Auto-encoder Networks for Cross-view Classification

no code implementations10 Feb 2014 Wen Wang, Zhen Cui, Hong Chang, Shiguang Shan, Xilin Chen

In this paper, we propose a simple but effective coupled neural network, called Deeply Coupled Autoencoder Networks (DCAN), which seeks to build two deep neural networks, coupled with each other in every corresponding layers.

Denoising General Classification +1

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