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DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection

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arXiv 2023年

作者： Fan, Cunhang Zhang, Hongyu Huang, Wei Xue, Jun Tao, Jianhua Yi, Jiangyan Lv, Zhao Wu, Xiaopei The Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China The National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Department of Automation Tsinghua University Beijing100190 China

Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Although EEG-based AAD methods have shown promising results in recent years, current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This makes it challenging to handle EEG signals, which possess non-Euclidean characteristics. In order to address this problem, this paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input. Specifically, to effectively represent the non-Euclidean properties of EEG signals, dynamical graph convolutional networks are applied to represent the graph structure of EEG signals, which can also extract crucial features related to auditory spatial attention in EEG signals. In addition, to further improve AAD detection performance, self-distillation, consisting of feature distillation and hierarchical distillation strategies at each layer, is integrated. These strategies leverage features and classification results from the deepest network layers to guide the learning of shallow layers. Our experiments are conducted on two publicly available datasets, KUL and DTU. Under a 1-second time window, we achieve results of 90.0% and 79.6% accuracy on KUL and DTU, respectively. We compare our DGSD method with competitive baselines, and the experimental results indicate that the detection performance of our proposed DGSD method is not only superior to the best reproducible baseline but also significantly reduces the number of trainable parameters by approximately 100 times. Copyright © 2023, The Authors. All rights reserved.

关键词： Electroencephalography

Multiple domain experts collaborative learning: Multi-source domain generalization for person re-identification

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arXiv 2021年

作者： Yu, Shijie Zhu, Feng Chen, Dapeng Zhao, Rui Chen, Haobin Zhu, Jinguo Tang, Shixiang Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences China SenseTime Group Limited Shanghai AI Lab Shanghai China Qing Yuan Research Institute Shanghai Jiao Tong University Shanghai China

Recent years have witnessed significant progress in person re-identification (ReID). However, current ReID approaches still suffer from considerable performance degradation when unseen testing domains exhibit different characteristics from the source training ones, known as the domain generalization problem. Given multiple source training domains, previous Domain Generalizable ReID (DG-ReID) methods usually learn all domains together using a shared network, which can't learn sufficient knowledge from each domain. In this paper, we propose a novel Multiple Domain Experts Collaborative Learning (MECL) framework for better exploiting all training domains, which benefits from the proposed Domain-Domain Collaborative Learning (DDCL) and Universal-Domain Collaborative Learning (UDCL). DDCL utilizes domain-specific experts for fully exploiting each domain, and prevents experts from over-fitting the corresponding domain using a meta-learning strategy. In UDCL, a universal expert supervises the learning of domain experts and continuously gathers knowledge from all domain experts. Note, only the universal expert will be used for inference. Extensive experiments on DG-ReID benchmarks demonstrate the effectiveness of DDCL and UDCL, and show that the whole MECL framework significantly outperforms state-of-the-arts. Experimental results on DG-classification benchmarks also reveal the great potential of applying MECL to other DG tasks. Code will be released. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

LSSLP – Local structure sensitive label propagation

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Information Sciences 2016年 332卷 19-32页

作者： Zhenfeng Zhu Jian Cheng Yao Zhao Jieping Ye Institute of Information Science Beijing Jiaotong University Beijing 100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing 100044 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences (CAS) 100190 China Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109-2218 USA

Label propagation is an approach to iteratively spread the prior state of label confidence associated with each of samples to its neighbors until achieving a global convergence state. Such process has been shown to hold close connection with a general graph-based regularization framework. Within this framework, a closed- form linear system can be built to carry out label propagation. In this paper, to address several issues inherent with previous graph-based label propagation framework, we propose a reformulated one, i.e., local structure sensitive label propagation ( LSSLP ). By associating each graph vertex with a local structure sensitive tuning factor, the empirical loss error on each vertex can be controlled preferably to keep consistent with the commonly preconditioned ‘ cluster assumption ’ of data structure. Out of consideration for information conservation, we relax the state conservation constraint of label confidence from labeled samples proposed by Belkin et al. (2004) to a more general form. Meanwhile, an inverse-warping procedure is incorporated into the proposed local structure sensitive label propagation framework to maintain large and stable enough classification margin. Based on the felicitous inversion technique for blocked matrix, we extend LSSLP to its incremental and inductive versions and also present computationally efficient implementation of it. Experimental results demonstrate the performance of the reformulated regularization framework for label propagation is much competitive.

关键词：

Local Neighbor Propagation on Graphs for Robust Feature Matching

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SSRN

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

Riemannian Self-Attention Mechanism for SPD Networks

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arXiv 2023年

作者： Wang, Rui Wu, Xiao-Jun Li, Hui Kittler, Josef School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

Symmetric positive definite (SPD) matrix has been demonstrated to be an effective feature descriptor in many scientific areas, as it can encode spatiotemporal statistics of the data adequately on a curved Riemannian manifold, i.e., SPD manifold. Although there are many different ways to design network architectures for SPD matrix nonlinear learning, very few solutions explicitly mine the geometrical dependencies of features at different layers. Motivated by the great success of self-attention mechanism in capturing long-range relationships, an SPD manifold self-attention mechanism (SMSA) is proposed in this paper using some manifold-valued geometric operations, mainly the Riemannian metric, Riemannian mean, and Riemannian optimization. Then, an SMSA-based geometric learning module (SMSA-GLM) is designed for the sake of improving the discrimination of the generated deep structured representations. Extensive experimental results achieved on three benchmarking datasets show that our modification against the baseline network further alleviates the information degradation problem and leads to improved accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Network architecture

Let's Play Music: Audio-Driven Performance Video Generation

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Let's Play Music: Audio-Driven Performance Video Generation

International Conference on pattern recognition

作者： Hao Zhu Yi Li Feixia Zhu Aihua Zheng Ran He Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) CASIA Beijing China Center for Excellence in Brain Science and Intelligence Technology CAS Beijing China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University

We propose a new task named Audio-driven Performance Video Generation (APVG), which aims to synthesize the video of a person playing a certain instrument guided by a given music audio clip. It is a challenging task to generate the high-dimensional temporal consistent videos from low-dimensional audio modality. In this paper, we propose a multi-staged framework to generate realistic and synchronized performance video from given music. Firstly, we provide both global appearance and local spatial information by generating the coarse videos and keypoints of body and hands from a given music respectively. Then, we propose to transform the generated keypoints to heatmap via a differentiable space transformer, since the heatmap provides more spatial information but is harder to generate directly from audio. Finally, we propose a Structured Temporal UNet (STU) to extract both intra-frame structured information and interframe temporal consistency. They are obtained via graph-based structure module, and CNN-GRU based high-level temporal module respectively for final video generation. Comprehensive experiments validate the effectiveness of our proposed framework.

关键词： Instruments Transforms Synchronization Data mining Task analysis Space heating

Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition

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arXiv 2021年

作者： Zhou, Ling Mao, Qirong Huang, Xiaohua Zhang, Feifei Zhang, Zhihong School of Computer Science and Communication Engineering Jiangsu University ZhenjiangJiangsu212013 China School of Computer Engineering Nanjing Institute of Technology China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Xiamen University Xiamen China Center for Machine Vision and Signal Analysis University of Oulu Finland

Micro-Expression recognition has become challenging, as it is extremely difficult to extract the subtle facial changes of micro-expressions. Recently, several approaches proposed several expression-shared features algorithms for micro-expression recognition. However, they do not reveal the specific discriminative characteristics, which lead to sub-optimal performance. This paper proposes a novel Feature Refinement (FR) with expression-specific feature learning and fusion for micro-expression recognition. It aims to obtain salient and discriminative features for specific expressions and also predict expression by fusing the expression-specific features. FR consists of an expression proposal module with attention mechanism and a classification branch. First, an inception module is designed based on optical flow to obtain expression-shared features. Second, in order to extract salient and discriminative features for specific expression, expression-shared features are fed into an expression proposal module with attention factors and proposal loss. Last, in the classification branch, labels of categories are predicted by a fusion of the expression-specific features. Experiments on three publicly available databases validate the effectiveness of FR under different protocol. Results on public benchmarks demonstrate that our FR provides salient and discriminative information for micro-expression recognition. The results also show our FR achieves better or competitive performance with the existing state-of-the-art methods on micro-expression recognition. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

A Novel Ordinal Regression Method with Minimum Class Variance Support Vector Machine

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A Novel Ordinal Regression Method with Minimum Class Varianc...

2015 International Conference on Materials Engineering and Information Technology Applications(MEITA 2015)

作者： Jinrong Hu Xiaoming Wang Zengxi Huang School of Computer and Soft Engineering Xihua University Key Laboratory of Pattern Recognition and Intelligent Information Processing Chengdu University

ISBN: (纸本)9781510812055

In the paper, we propose a novel ordinal regression method called minimum class variance support vector ordinal regression(MCVSVOR). MCVSVOR is derived from minimum class variance support vector machine(MCVSVM) which is a variant of SVM, and so inherits the latter's characteristics such as taking the distribution of the categories into consideration and good generalization performance. Finally, the experimental results validate the effectiveness of MCVSVOR and indicate its superior generalization performance over SVOR.

关键词： Machine learning Ordinal regression Support vector machine Support vector ordinal regression

Fine-Grained Topography and Modularity of the Macaque Frontal Pole Cortex Revealed by Anatomical Connectivity Profiles

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Neuroscience Bulletin 2020年第12期36卷 1454-1473页

作者： Bin He Long Cao Xiaoluan Xia Baogui Zhang Dan Zhang Bo You Lingzhong Fan Tianzi Jiang School of Mechanical and Power Engineering Harbin University of Science and TechnologyHarbin 150080China Brainnetome Center Institute of AutomationChinese Academy of SciencesBeijing 100190China National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of Sciences(CAS)Beijing 100190China Center for Excellence in Brain Science and Intelligence Technology Institute of AutomationCASBeijing 100190China Key Laboratory for Neuroinformation of the Ministry of Education School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu 610054China The Queensland Brain Institute University of QueenslandBrisbaneQLD 4072Australia University of CAS Beijing 100049China College of Information and Computer Taiyuan University of TechnologyTaiyuan 030600China Chinese Institute for Brain Research Beijing 102206China Core Facility Center of Biomedical AnalysisTsinghua UniversityBeijing 100084China

The frontal pole cortex(FPC)plays key roles in various higher-order functions and is highly developed in non-human *** essential missing piece of information is the detailed anatomical connections for finer parcellation of the macaque FPC than provided by the previous tracer *** is important for understanding the functional architecture of the cerebral ***,combining cross-validation and principal component analysis,we formed a tractography-based parcellation scheme that applied a machine learning algorithm to divide the macaque FPC(2 males and 6 females)into eight subareas using high-resolution diffusion magnetic resonance imaging with the 9.4 T Bruker system,and then revealed their subregional ***,we applied improved hierarchical clustering to the obtained parcels to probe the modular structure of the subregions,and found that the dorsolateral FPC,which contains an extension to the medial FPC,was mainly connected to regions of the default-mode *** ventral FPC was mainly involved in the social-interaction network and the dorsal FPC in the metacognitive *** results enhance our understanding of the anatomy and circuitry of the macaque brain,and contribute to FPC-related clinical research.

关键词： Macaque Frontal pole cortex Anatomical connectivity profile Parcellation Neuroimaging Principal component analysis