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arXiv 2022年

作者： Ding, Yongliang Zhou, Tao Zhang, Chuang Luo, Yijing Tang, Juan Gong, Chen PCA Lab The Key Laboratory of Intelligent Perceptron and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Cyber Engineering Guangzhou University China

In real-world scenarios, many large-scale datasets often contain inaccurate labels, i.e., noisy labels, which may confuse model training and lead to performance degradation. To overcome this issue, Label Noise Learning (LNL) has recently attracted much attention, and various methods have been proposed to design an unbiased risk estimator to the noise-free dataset to combat such label noise. Among them, a trend of works based on Loss Decomposition and Centroid Estimation (LDCE) has shown very promising performance. However, existing LNL methods based on LDCE are only designed for binary classification, and they are not directly extendable to multi-class situations. In this paper, we propose a novel multi-class robust learning method for LDCE, which is termed "MC-LDCE". Specifically, we decompose the commonly adopted loss (e.g., mean squared loss) function into a label-dependent part and a label-independent part, in which only the former is influenced by label noise. Further, by defining a new form of data centroid, we transform the recovery problem of a label-dependent part to a centroid estimation problem. Finally, by critically examining the mathematical expectation of clean data centroid given the observed noisy set, the centroid can be estimated which helps to build an unbiased risk estimator for multi-class learning. The proposed MC-LDCE method is general and applicable to different types (i.e., linear and nonlinear) of classification models. The experimental results on five public datasets demonstrate the superiority of the proposed MC-LDCE against other representative LNL methods in tackling multi-class label noise problem. © 2022, CC BY-NC-SA.

关键词： Learning systems

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Parametric Model Estimation for 3D Clothed Humans from Point Clouds

Parametric Model Estimation for 3D Clothed Humans from Point...

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International Symposium on Mixed and Augmented Reality (ISMAR)

作者： Kangkan Wang Huayu Zheng Guofeng Zhang Jian Yang Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China

This paper presents a novel framework to estimate parametric model- s for 3D clothed humans from partial point clouds. It is a challenging problem due to factors such as arbitrary human shape and pose, large variations in clothing details, and significant missing data. Existing methods mainly focus on estimating the parametric model of undressed bodies or reconstructing the non-parametric 3D shapes from point clouds. In this paper, we propose a hierarchical regression framework to learn the parametric model of detailed human shapes from partial point clouds of a single depth frame. Benefiting from the favorable ability of deep neural networks to model nonlinearity, the proposed framework cascades several successive regression networks to estimate the parameters of detailed 3D human body models in a coarse-to-fine manner. Specifically, the first global regression network extracts global deep features of point clouds to obtain an initial estimation of the undressed human model. Based on the initial estimation, the local regression network then refines the undressed human model by using the local features of neighborhood points of human joints. Finally, the clothing details are inferred as an additive displacement on the refined undressed model using the vertex-level regression network. The experimental results demonstrate that the proposed hierarchical regression approach can accurately predict detailed human shapes from partial point clouds and outperform prior works in the recovery accuracy of 3D human models.

关键词： Deep learning Solid modeling Three-dimensional displays Shape Clothing Estimation Predictive models

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Hybrid Data-Free Knowledge Distillation

arXiv

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arXiv 2024年

作者： Tang, Jialiang Chen, Shuo Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Center for Advanced Intelligence Project RIKEN Japan Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generation-based methods train student networks by collecting massive real examples and generating synthetic examples, respectively. However, they inevitably become weak in practical scenarios due to the difficulties in gathering or emulating sufficient real-world data. To solve this problem, we propose a novel method called Hybrid Data-Free Distillation (HiDFD), which leverages only a small amount of collected data as well as generates sufficient examples for training student networks. Our HiDFD comprises two primary modules, i.e., the teacher-guided generation and student distillation. The teacher-guided generation module guides a Generative Adversarial Network (GAN) by the teacher network to produce high-quality synthetic examples from very few real-world collected examples. Specifically, we design a feature integration mechanism to prevent the GAN from overfitting and facilitate the reliable representation learning from the teacher network. Meanwhile, we drive a category frequency smoothing technique via the teacher network to balance the generative training of each category. In the student distillation module, we explore a data inflation strategy to properly utilize a blend of real and synthetic data to train the student network via a classifier-sharing-based feature alignment technique. Intensive experiments across multiple benchmarks demonstrate that our HiDFD can achieve state-of-the-art performance using 120 times less collected data than existing methods. Code is available at https://***/tangjialiang97/HiDFD. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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Dual sentence representation model integrating prior knowledge for bio-text-mining

Dual sentence representation model integrating prior knowled...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhijing Li Yangyang Lan Saikat Chatterjee Pargorn Puttapirat Xiangrong Zhang Chen Li School of Computer Science and Technology Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech Xi’an Jiaotong University Xi’an China Communication Theory Laboratory School of Electrical Engineering KTH Royal Institute of Technology Stockholm Sweden The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi’an China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Data mining, especially the extraction of the relationship between genes and proteins, plays an important role in the biomedical field. Several related models have been proposed for data mining in the biomedical domain. Furthermore, manually curated biomedical knowledge bases, which could assist the task, have been used to enhance the data-mining model. However, due to the limitation of methods, much prior knowledge information is not be fully exploited. In this work, we propose a novel method that reasonably applied the curated prior knowledge for biomedical text mining by dual sentence representation models; one model is for the experimental data and the other one is for the prior knowledge information sentence. We evaluated our method on two community-supported datasets; BioNLP and BioCreative corpora. The experimental results demonstrate that the dual sentence representation model can successfully utilize external prior knowledge information to extract relationship from biomedical text. Our method can achieve state-of-art results and it could be an application of biomedical relation extraction in the future.

关键词： Biological system modeling Task analysis Proteins Semantics Knowledge engineering Data models Biology

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Bayesian Layer Graph Convolutional Network for Hyperspetral image Classification

arXiv

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arXiv 2022年

作者： Zhang, Mingyang Di, Ziqi Gong, Maoguo Wu, Yue Li, Hao Jiang, Xiangming The School of Electronic Engineering The Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University No. 2 South TaiBai Road Xi’an710071 China The School of Computer Science and Technology Xidian University Xi’an710071 China

In recent years, research on hyperspectral image (HSI) classification has continuous progress on introducing deep network models, and recently the graph convolutional network (GCN) based models have shown impressive performance. However, these deep learning frameworks are based on point estimation suffering from low generalization and inability to quantify the classification results uncertainty. On the other hand, simply applying the distribution estimation based Bayesian Neural Network (BNN) to classify the HSI is unable to achieve high classification efficiency due to the large amount of parameters. In this paper, we design a Bayesian layer as an insertion layer into point estimation based neural networks, and propose a Bayesian layer graph convolutional network (BLGCN) model by combining graph convolution operations, which can effectively extract graph information and estimate the uncertainty of classification results. Moreover, a Generative Adversarial Network (GAN) is built to solve the sample imbalance problem of HSI dataset. Finally, we design a dynamic control training strategy based on the confidence interval of the classification results, which will terminate the training early when the confidence interval reaches the presented threshold. The experimental results show that our model achieves a balance between high classification accuracy and strong generalization. In addition, it can quantify the uncertainty of outputs. © 2022, CC BY.

关键词： Generative adversarial networks

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Endmember Selection of Hyperspectral images based on Evolutionary Multitask

Endmember Selection of Hyperspectral Images based on Evoluti...

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Congress on Evolutionary Computation

作者： Yizhe Zhao Hao Li Yue Wu Shanfeng Wang Maoguo Gong School of Electronic Engineering Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi’an China School of Computer Science and Technology Xidian University Xi’an China School of Cyber Engineering Xidian University Xi’an China

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

Endmember selection of hyperspectral images is a practical yet difficult task due to the high spectral resolution and low spatial resolution of the hyperspectral cameras. The paradigm of multitask optimization has been investigated over two decades, which aim to handle multiple tasks simultaneously. To address these issues, we propose a novel multitasking framework based on multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D). Specifically, we use a single population to simultaneously perform multiple subset selection tasks and apply it to a specific scene-the endmember selection of hyperspectral images. It is natural to consider that pixels in a homogeneous region of hyperspectral image as a task. Then, a within-task and between-task genetic transfer operator is constructed to reinforce the exchange of genetic material belonging to the same or different tasks for better and quicker search of the decision space. After that, this algorithm obtains a set of nondominated solutions for better decision of the active endmembers. Experiments on hyperspectral datasets show the effectiveness of our method in finding the real active endmembers.

关键词： Hyperspectral imaging Task analysis Libraries Multitasking Pareto optimization

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Multi-scale dynamic graph convolutional network for hyperspectral image classification

arXiv

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arXiv 2019年

作者： Wan, Sheng Gong, Chen Zhong, Ping Du, Bo Zhang, Lefei Yang, Jian PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China National Key Laboratory of Science and Technology on ATR National University of Defense Technology Changsha410073 China State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan430079 China

Convolutional Neural Network (CNN) has demonstrated impressive ability to represent hyperspectral images and to achieve promising results in hyperspectral image classification. However, traditional CNN models can only operate convolution on regular square image regions with fixed size and weights, so they cannot universally adapt to the distinct local regions with various object distributions and geometric appearances. Therefore, their classification performances are still to be improved, especially in class boundaries. To alleviate this shortcoming, we consider employing the recently proposed Graph Convolutional Network (GCN) for hyperspectral image classification, as it can conduct the convolution on arbitrarily structured non-Euclidean data and is applicable to the irregular image regions represented by graph topological information. Different from the commonly used GCN models which work on a fixed graph, we enable the graph to be dynamically updated along with the graph convolution process, so that these two steps can be benefited from each other to gradually produce the discriminative embedded features as well as a refined graph. Moreover, to comprehensively deploy the multi-scale information inherited by hyperspectral images, we establish multiple input graphs with different neighborhood scales to extensively exploit the diversified spectral-spatial correlations at multiple scales. Therefore, our method is termed 'Multi-scale Dynamic Graph Convolutional Network' (MDGCN). The experimental results on three typical benchmark datasets firmly demonstrate the superiority of the proposed MDGCN to other state-of-the-art methods in both qualitative and quantitative aspects. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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2D License Plate Recognition based on Automatic Perspective Rectification

2D License Plate Recognition based on Automatic Perspective ...

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International Conference on Pattern Recognition

作者： Hui Xu Zhao-Hong Guo Da-Han Wang Xiang-Dong Zhou Yu Shi Chongqing school University of Chinese Academy of Sciences china Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences

License plate recognition (LPR) remains a challenging task in face of some difficulties such as image deformation and multi-line character distribution. Text rectification that is crucial to eliminate the effects of image deformation has attracted increasing attentions in scene text recognition. However, current text rectification methods are not designed specifically for LPR, which did not take the features of plate deformation into account. Considering the fact that a license plate (LP) can only generate perspective distortion in the image due to its rigid feature, in this paper we propose a novel perspective rectification network (PRN) to automatically estimate the perspective transformation and rectify the distorted LP accordingly. For recognition, we propose a location-aware 2D attention based recognition network that is capable of recognizing both single-line and double-line plates with perspective deformation. The rectification network and recognition network are connected for end-to-end training. Experiments on common datasets show that the proposed method achieves the state-of-the-art performance, demonstrating the effectiveness of the proposed approach.

关键词： Training Text recognition Face recognition Licenses Benchmark testing Distortion Optical character recognition software

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Joint Semantic Segmentation Using Representations of Lidar Point Clouds and Camera images

SSRN

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SSRN 2023年

作者： Wu, Yue Liu, Jiaming Gong, Maoguo Miao, Qiguang Ma, Wenping Xu, Cai School of Computer Science and Technology Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China School of Artificial Intelligence Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi’an710071 China

LiDAR and camera are two common vision sensors used in the real world, producing complementary point cloud and image data. While multimodal data has previously been found mostly in 3D detection and tracking, we aim to study large-scale semantic segmentation by multimodal data fusion rather than only knowledge transfer or distillation. We show that fusing LiDAR features with camera features and abandoning the strict point-to-pixel hard correlation can lead to better performance. Even so, it is still difficult to make full use of multimodal data due to the spatiotemporal misalignment of sensors and uneven data distribution.. To address this issue, we propose the Joint Semantic Segmentation (JoSS), a powerful LiDAR-camera fusion solution that employs the attention mechanism to explore the potential relationships between point clouds and images. Specifically, JoSS consists of commonly used 3D and 2D backbones, and lightweight transformer decoders based on point clouds and images. A point cloud decoder adopts queries to analyze the semantics from LiDAR features, and an image decoder adaptively fuses these queries with corresponding image features. Both exploit contextual information, thus fully mining multimodal information for semantic segmentation. In addition, we propose an effective unimodal data augmentation (UDA) method that performs cross-modal contrastive learning on point clouds and images to significantly improve accuracy by augmenting the point cloud alone without the complexity of generating paired samples of both modalities. Our Joss achieves advanced results in two widely used large-scale benchmarks, i.e. SemanticKITTI and nuScenes-lidarseg. © 2023, The Authors. All rights reserved.

关键词： Semantics

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Local Neighbor Propagation on Graphs for Robust Feature Matching

SSRN

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SSRN 2023年

作者： Guo, Hanlin Xiao, Guobao Su, Lumei Zhou, Jiaxing Wang, Dahan Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control School of Electrical Engineering and Automation Xiamen University of Technology China Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Information Science and Engineering Xiamen University China College of Computer and Control Engineering Minjiang University China Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology China

Establishing reliable correspondences between two sets of feature points is a critical preprocessing step in many computer vision and pattern recognition tasks. In this paper, we propose a novel robust Local Neighbor Propagation on Graphs based feature matching (LNPG) method, to obtain good correspondences for feature matching. LNPG starts from a novel neighborhood graph construction strategy. The strategy leverages the spatial consistency constraint to generate a series of neighborhood sets, and employs the residual information to preserve the local neighborhood relationships of potential inliers (i.e., true matches). Subsequently, LNPG incorporates local neighbor propagation into the graph to enhance connections between data in different neighborhoods, by using the path-based similarity measurement and the adaptive graph partition. In addition, LNPG includes a novel consistency-filtering-based clustering algorithm for robust feature matching. This clustering algorithm introduces a reliable neighborhood consistency measure function and an effective cluster merging criterion for cluster detection and cluster merging during the clustering process, respectively. Overall, LNPG not only effectively distinguishes inliers from outliers, but also reliably classifies inliers into different transformation models between pairs of images. Experiments on publicly available datasets with different types of image transformations show the superiority of our LNPG in comparison with other state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

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