Search Results for author: Chunyan Xu

Found 24 papers, 5 papers with code

Few-shot Continual Infomax Learning

no code implementations ICCV 2023 Ziqi Gu, Chunyan Xu, Jian Yang, Zhen Cui

Further, considering that the learned knowledge in the human brain is a generalization of actual information and exists in a certain relational structure, we perform continual structure infomax learning to relieve the catastrophic forgetting problem in the continual learning process.

Continual Learning Few-Shot Learning

CVNet: Contour Vibration Network for Building Extraction

1 code implementation CVPR 2022 Ziqiang Xu, Chunyan Xu, Zhen Cui, Xiangwei Zheng, Jian Yang

The classic active contour model raises a great promising solution to polygon-based object extraction with the progress of deep learning recently.

Model Optimization

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 Video 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 +1

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.

Management Tensor Decomposition +1

Scribble-Supervised Semantic Segmentation Inference

no code implementations ICCV 2021 Jingshan Xu, Chuanwei Zhou, Zhen Cui, Chunyan Xu, Yuge Huang, Pengcheng Shen, Shaoxin Li, Jian Yang

In this paper, we propose a progressive segmentation inference (PSI) framework to tackle with scribble-supervised semantic segmentation.

Segmentation Semantic Segmentation

Attention-Aware Noisy Label Learning for Image Classification

no code implementations30 Sep 2020 Zhenzhen Wang, Chunyan Xu, Yap-Peng Tan, Junsong Yuan

In this paper, the attention-aware noisy label learning approach ($A^2NL$) is proposed to improve the discriminative capability of the network trained on datasets with potential label noise.

Classification General Classification +2

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

1 code implementation20 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 In Surveillance Videos Video 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.

Classification General Classification +2

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.

Classification General Classification +1

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 Object Recognition +2

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 Segmentation +1

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.

Clustering General Classification +2

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 +1

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 Analysis Action Recognition +3

Deep Learning with S-shaped Rectified Linear Activation Units

1 code implementation22 Dec 2015 Xiaojie Jin, Chunyan Xu, Jiashi Feng, Yunchao Wei, Junjun Xiong, Shuicheng Yan

Rectified linear activation units are important components for state-of-the-art deep convolutional networks.

Human Parsing With Contextualized Convolutional Neural Network

no code implementations ICCV 2015 Xiaodan Liang, Chunyan Xu, Xiaohui Shen, Jianchao Yang, Si Liu, Jinhui Tang, Liang Lin, Shuicheng Yan

In this work, we address the human parsing task with a novel Contextualized Convolutional Neural Network (Co-CNN) architecture, which well integrates the cross-layer context, global image-level context, within-super-pixel context and cross-super-pixel neighborhood context into a unified network.

Human Parsing

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 regression

Generalized Singular Value Thresholding

no code implementations6 Dec 2014 Canyi Lu, Changbo Zhu, Chunyan Xu, Shuicheng Yan, Zhouchen Lin

This work studies the Generalized Singular Value Thresholding (GSVT) operator ${\text{Prox}}_{g}^{{\sigma}}(\cdot)$, \begin{equation*} {\text{Prox}}_{g}^{{\sigma}}(B)=\arg\min\limits_{X}\sum_{i=1}^{m}g(\sigma_{i}(X)) + \frac{1}{2}||X-B||_{F}^{2}, \end{equation*} associated with a nonconvex function $g$ defined on the singular values of $X$.

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