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arXiv 2022年

作者： Yang, Guang Chen, Xingguo Yang, Shangdong Wang, Huihui Dong, Shaokang Gao, Yang The the Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications National Engineering Laboratory for Agri-Product Quality Traceability Beijing Technology and Business University China The State Key Laboratory for Novel Software Technology Nanjing University China The PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information 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

With rising uncertainty in the real world, online Reinforcement Learning (RL) has been receiving increasing attention due to its fast learning capability and improving data efficiency. However, online RL often suffers from complex Value Function Approximation (VFA) and catastrophic interference, creating difficulty for the deep neural network to be applied to an online RL algorithm in a fully online setting. Therefore, a simpler and more adaptive approach is introduced to evaluate value function with the kernel-based model. Sparse representations are superior at handling interference, indicating that competitive sparse representations should be learnable, non-prior, non-truncated and explicit when compared with current sparse representation methods. Moreover, in learning sparse representations, attention mechanisms are utilized to represent the degree of sparsification, and a smooth attentive function is introduced into the kernel-based VFA. In this paper, we propose an Online Attentive Kernel-Based Temporal Difference (OAKTD) algorithm using two-timescale optimization and provide convergence analysis of our proposed algorithm. Experimental evaluations showed that OAKTD outperformed several Online Kernel-based Temporal Difference (OKTD) learning algorithms in addition to the Temporal Difference (TD) learning algorithm with Tile Coding on public Mountain Car, Acrobot, CartPole and Puddle World tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

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Accurate quaternion radial harmonic Fourier moments for color image reconstruction and object recognition

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Pattern Analysis and Applications 2020年第4期23卷 1551-1567页

作者： Liu, Yunan Zhang, Shanshan Li, Guangyu Wang, Houjun Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China National Ocean Technology Center Tianjin 300112 China

Orthogonal moments have become a powerful tool for object representation and image analysis. Radial harmonic Fourier moments (RHFMs) are one of such image descriptors based on a set of orthogonal projection bases, which outperform other moments because of their computational efficiency. However, the conventional computational framework of RHFMs produces geometric error and numerical integration error, which will affect the accuracy of RHFMs, thus degrading the image reconstruction performance. To overcome this shortcoming, we propose a new computational framework of RHFMs, namely accurate quaternion radial harmonic Fourier moments (AQRHFMs), for color image processing, and also analyze the properties of AQRHFMs. Firstly, we propose a precise computation method of RHFMs to reduce the geometric and numerical errors. Secondly, by using the algebra of quaternions, we extend the accurate RHFMs to AQRHFMs in order to deal with the color images in a holistic manner. Experimental results show the proposed AQRHFMs achieve promising performance in image reconstruction and object recognition in both noise-free and noisy conditions. © 2020, Springer-Verlag London Ltd., part of Springer Nature.

关键词： Geometric error Numerical error Object recognition Quaternion Radial harmonic Fourier moments

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An attention-based unsupervised adversarial model for movie review spam detection

arXiv

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arXiv 2021年

作者： Gao, Yuan Gong, Maoguo Xie, Yu Qin, A.K. The School of Electronic Engineering Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi'anShanxi Province710071 China The department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia

With the prevalence of the Internet, online reviews have become a valuable information resource for people. However, the authenticity of online reviews remains a concern, and deceptive reviews have become one of the most urgent network security problems to be solved. Review spams will mislead users into making suboptimal choices and inflict their trust in online reviews. Most existing research manually extracted features and labeled training samples, which are usually complicated and time-consuming. This paper focuses primarily on a neglected emerging domain - movie review, and develops a novel unsupervised spam detection model with an attention mechanism. By extracting the statistical features of reviews, it is revealed that users will express their sentiments on different aspects of movies in reviews. An attention mechanism is introduced in the review embedding, and the conditional generative adversarial network is exploited to learn users' review style for different genres of movies. The proposed model is evaluated on movie reviews crawled from Douban, a Chinese online community where people could express their feelings about movies. The experimental results demonstrate the superior performance of the proposed approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Congested crowd instance localization with dilated convolutional swin transformer

arXiv

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arXiv 2021年

作者： Gao, Junyu Gong, Maoguo Li, Xuelong The Academy of Advanced Interdisciplinary Research Xidian University Shaanxi Xi'an710071 China Northwestern Polytechnical University Xi'an710072 China The Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education International Research Center for Intelligent Perception and Computation Xidian University Shaanxi Xi'an710071 China

Crowd localization is a new computer vision task, evolved from crowd counting. Different from the latter, it provides more precise location information for each instance, not just counting numbers for the whole crowd scene, which brings greater challenges, especially in extremely congested crowd scenes. In this paper, we focus on how to achieve precise instance localization in high-density crowd scenes, and to alleviate the problem that the feature extraction ability of the traditional model is reduced due to the target occlusion, the image blur, etc. To this end, we propose a Dilated Convolutional Swin Transformer (DCST) for congested crowd scenes. Specifically, a window-based vision transformer is introduced into the crowd localization task, which effectively improves the capacity of representation learning. Then, the well-designed dilated convolutional module is inserted into some different stages of the transformer to enhance the large-range contextual information. Extensive experiments evidence the effectiveness of the proposed methods and achieve the state-of-the-art performance on five popular datasets. Especially, the proposed model achieves F1-measure of 77.5% and MAE of 84.2 in terms of localization and counting performance, respectively. © 2021, CC BY.

关键词： Convolution

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GRU-TV: Time- and velocity-aware GRU for patient representation on multivariate clinical time-series data

arXiv

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arXiv 2022年

作者： Liu, Ningtao Gao, Ruoxi Yuan, Jing Park, Calire Xing, Shuwei Gou, Shuiping Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University Xi'An710071 China Robarts Research Institute Western University LondonONN6A 5B7 Canada Electrical Engineering and Computer Science Department University of Michigan Ann ArborMI48109 United States

Electronic health records (EHRs) are usually highly dimensional, heterogeneous, and multimodal. Besides, the random recording of clinical variables results in high missing rates and uneven time intervals between adjacent records in the multivariate clinical time-series data extracted from EHRs. Current works using clinical time-series data for patient representation regard the patients' physiological status as a discrete process described by sporadically collected records. However, changes in the patient's physiological condition are continuous and dynamic processes. The perception of time and velocity of change is crucial for patient representation learning. In this study, we propose a time- and velocity-aware gated recurrent unit model (GRU-TV) for patient representation learning of clinical multivariate time-series data in a time-continuous manner. The neural ordinary differential equations (ODEs) and velocity perception mechanism are applied to perceive the time interval between adjacent records and changing rate of the patient's physiological status, respectively. Our experiments on two real clinical EHR datasets (PhysioNet2012, MIMIC-III) establish that GRU-TV is a robust model on computer-aided diagnosis (CAD) tasks, especially on sequences with high-variance time intervals. Copyright © 2022, The Authors. All rights reserved.

关键词： Velocity

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SAR image Change Detection Method via a Pyramid Pooling Convolutional Neural Network

SAR Image Change Detection Method via a Pyramid Pooling Conv...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Rongfang Wang Fan Ding Jia-Wei Chen Bo Liu Jie Zhang Licheng Jiao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University Xi'an China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

In synthetic aperture radar (SAR) image change detection, it is quite challenging to exploit the changing information from the noisy difference image subject to the speckle. In this paper, we propose a novel mutli-scale average pooling (MSAP) network to exploit the changed information from the noisy difference image. Being different from traditional convolutional network with only an one-scale pooling kernel, in the proposed method, multi -scale pooling kernels are equipped in convolutional network to obtain the spatial context information on changed regions from the difference image. Finally, we verify our proposed method on four challenging datasets of bitemporal SAR images. Experimental results demonstrate that the difference map obtained by our proposed method outperforms than other state-of-art methods.

关键词： Radar polarimetry Training Synthetic aperture radar Noise measurement Kernel Speckle Convolution

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OTFace: Hard Samples Guided Optimal Transport Loss for Deep Face Representation

arXiv

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arXiv 2022年

作者： Qian, Jianjun Zhu, Shumin Zhao, Chaoyu Yang, Jian Wong, Wai Keung PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Infor. 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 Jiangsu Nanjing China Institute of Textiles and Clothing The Hong Kong Polytechnic University Hong Kong

Face representation in the wild is extremely hard due to the large scale face variations. To this end, some deep convolutional neural networks (CNNs) have been developed to learn discriminative feature by designing properly margin-based losses, which perform well on easy samples but fail on hard samples. Based on this, some methods mainly adjust the weights of hard samples in training stage to improve the feature discrimination. However, these methods overlook the feature distribution property which may lead to better results since the miss-classified hard samples may be corrected by using the distribution metric. This paper proposes the hard samples guided optimal transport (OT) loss for deep face representation, OTFace for short. OTFace aims to enhance the performance of hard samples by introducing the feature distribution discrepancy while maintain the performance on easy samples. Specifically, we embrace triplet scheme to indicate hard sample groups in one mini-batch during training. OT is then used to characterize the distribution differences of features from the high level convolutional layer. Finally, we integrate the margin-based-softmax (e.g. ArcFace or AM-Softmax) and OT to guide deep CNN learning. Extensive experiments are conducted on several benchmark databases. The quantitative results demonstrate the advantages of the proposed OTFace over state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Audio-visual representation learning for anomaly events detection in crowds

arXiv

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arXiv 2021年

作者： Gao, Junyu Gong, Maoguo Li, Xuelong The Academy of Advanced Interdisciplinary Research Xidian University Shaanxi Xi’an710071 China The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Xidian University Shaanxi Xi’an710071 China Northwestern Polytechnical University Xi’an710072 China

In recent years, anomaly events detection in crowd scenes attracts many researchers’ attentions, because of its importance to public safety. Existing methods usually exploit visual information to analyze whether any abnormal events have occurred due to only visual sensors are generally equipped in public places. However, when an abnormal event in crowds occurs, sound information may be discriminative to assist the crowd analysis system to determine whether there is an abnormality. Compare with vision information that is easily occluded, audio signals has a certain degree of penetration. Thus, this paper attempt to exploit multi-modal learning for modeling the audio and visual signals simultaneously. To be specific, we design a two-branch network to model different types of information. The first is a typical 3D CNN model to extract temporal appearance feature from video clips. The second is an audio CNN for encoding Log Mel-Spectrogram of audio signals. Finally, by fusing the above features, the more accurate prediction will be produced. We conduct the experiments on SHADE dataset, a synthetic audio-visual dataset in surveillance scenes, and find introducing audio signals effectively improves the performance of anomaly events detection and outperforms other state-of-the-art methods. Furthermore, we will release the code and the pre-trained models as soon as possible. © 2021, CC BY.

关键词： Learning systems

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Heartbeat Detection and Rate Estimation from Ballistocardiograms using the Gated Recurrent Unit Network 42

Heartbeat Detection and Rate Estimation from Ballistocardiog...

引用

42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020

作者： Hai, Dong Chen, Chao Yi, Ruhan Gou, Shuiping Yu Su, Bo Jiao, Changzhe Skubic, Marjorie Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Ibm Research University of Missouri Eecs Dept. United States Brigham and Women Hospital Division of Sleep and Circadian Disorders Harvard Medical School United States

ISBN: (纸本)9781728119908

Inspired by the application of recurrent neural networks (RNNs) to image recognition, in this paper, we propose a heartbeat detection framework based on the Gated Recurrent Unit (GRU) network. In this contribution, the heartbeat detection task from ballistocardiogram (BCG) signals was modeled as a classification problem where the segments of BCG signals were formulated as images fed into the GRU network for feature extraction. The proposed framework has advantages in fusion of multi-channel BCG signals and effective extraction of the temporal and waveform characteristics of the heartbeat signal, thereby enhancing heart rate estimation accuracy. In laboratory collected BCG data, the proposed method achieved the best heart rate estimation results compared to previous algorithms. © 2020 IEEE.

关键词： Recurrent neural networks

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A Lightweight Convolutional Neural Network for Bitemporal image Change Detection

A Lightweight Convolutional Neural Network for Bitemporal Im...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Rongfang Wang Fan Ding Jia-Wei Chen Licheng Jiao Liang Wang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University Xi'an China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Recently, many convolution neural networks have been successfully employed in bitemporal SAR image change detection. However, most of those networks are too heavy where large memory are necessary for storage and calculation. To reduce the computational and spatial complexity and facilitate the change detection on edge devices, in this paper, we propose a lightweight neural network for bitemporal SAR image change detection. In the proposed network, we replace the regular convolutional layers with bottlenecks, which will not increase the number of channels. Furthermore, we employ dilated convolutional kernels with a few non-zero entries which reduces the FLOPs in convlutional operators. Comparing with traditional neural network, our lightweight neural network will be faster, less FLOPs and parameters. We verify our lightweight neural network on two sets of bitemporal SAR images. The experimental results show that the proposed network can obtain the comparable performance with those heavy-weight neural network.

关键词： Radar polarimetry Neural networks Convolution Synthetic aperture radar Kernel Visualization Noise measurement

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