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2nd CCF Chinese Conference on Computer Vision (CCCV)

作者： Hu, Dong Liu, Xin Peng, Shujuan Zhong, Bineng Du, Jixiang Huaqiao Univ Dept Comp Sci Xiamen 361021 Peoples R China Huaqiao Univ Xiamen Key Lab Comp Vis & Pattern Recognit Xiamen 361021 Peoples R China

ISBN: (纸本)9789811072994;9789811072987

The style of motion is essential for virtual characters animation, and it is significant to generate motion style efficiently in computer animation. In this paper, we present an efficient approach to automatically transfer motion style by using autoencoder generative model and spatio-temporal correlation mining, which allows users to transform an input motion into a new style while preserving its original content. To this end, we introduce a history vector of previous motion frames into autoencoder generative network, and extract the spatio-temporal feature of input motion. Accordingly, the spatio-temporal correlation within motions can be represented by the correlated hidden units in this network. Subsequently, we established the constraints of Gram matrix in such feature space to produce transferred motion by pre-trained generative model. As a result, various motions of particular semantic can be automatically transferred from one style to another one, and the extensive experiments have shown its outstanding performance.

关键词： deep autoencoder Style transfer Character animation Spatio-temporal correlation History vector

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Adversarially Learned Abnormal Trajectory Classifier 16

Adversarially Learned Abnormal Trajectory Classifier

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16th Conference on Computer and Robot Vision (CRV)

作者： Roy, Pankaj Raj Bilodeau, Guillaume-Alexandre Polytech Montreal LITIV Lab Montreal PQ Canada

ISBN: (纸本)9781728118383

We address the problem of abnormal event detection from trajectory data. In this paper, a new adversarial approach is proposed for building a deep neural network binary classifier, trained in an unsupervised fashion, that can distinguish normal from abnormal trajectory-based events without the need for setting manual detection threshold. Inspired by the generative adversarial network (GAN) framework, our GAN version is a discriminative one in which the discriminator is trained to distinguish normal and abnormal trajectory reconstruction errors given by a deep autoencoder. With urban traffic videos and their associated trajectories, our proposed method gives the best accuracy for abnormal trajectory detection. In addition, our model can easily be generalized for abnormal trajectory-based event detection and can still yield the best behavioural detection results as demonstrated on the CAVIAR dataset.

关键词： deep autoencoder Unsupervised learning Abnormal event detection Generative adversarial networks

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deep 3D reconstruction:methods,data,and challenges

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Frontiers of Information Technology & Electronic Engineering 2021年第5期22卷 652-672页

作者： Caixia LIU Dehui KONG Shaofan WANG Zhiyong WANG Jinghua LI Baocai YIN Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial IntelligenceFaculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Multimedia Laboratory School of Computer ScienceUniversity of SydneySydney NSW 2006Australia

Three-dimensional(3D)reconstruction of shapes is an important research topic in the fields of computer vision,computer graphics,pattern recognition,and virtual *** 3D reconstruction methods usually suffer from two bottlenecks:(1)they involve multiple manually designed states which can lead to cumulative errors,but can hardly learn semantic features of 3D shapes automatically;(2)they depend heavily on the content and quality of images,as well as precisely calibrated *** a result,it is difficult to improve the reconstruction accuracy of those methods.3D reconstruction methods based on deep learning overcome both of these bottlenecks by automatically learning semantic features of 3D shapes from low-quality images using deep ***,while these methods have various architectures,in-depth analysis and comparisons of them are unavailable so *** present a comprehensive survey of 3D reconstruction methods based on deep ***,based on different deep learning model architectures,we divide 3D reconstruction methods based on deep learning into four types,recurrent neural network,deep autoencoder,generative adversarial network,and convolutional neural network based methods,and analyze the corresponding methodologies ***,we investigate four representative databases that are commonly used by the above methods in ***,we give a comprehensive comparison of 3D reconstruction methods based on deep learning,which consists of the results of different methods with respect to the same database,the results of each method with respect to different databases,and the robustness of each method with respect to the number of ***,we discuss future development of 3D reconstruction methods based on deep learning.

关键词： deep learning models Three-dimensional reconstruction Recurrent neural network deep autoencoder Generative adversarial network Convolutional neural network

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Unsupervised Representation Learning Method for UAV's Scene Perception

Unsupervised Representation Learning Method for UAV's Scene ...

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9th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Yao Kaidi Ma Zhaowei Lei Jinhong Shen Sibo Zhu Yulin PLA Unit 63981 Wuhan Hubei Peoples R China Natl Univ Def Technol Changsha Hunan Peoples R China

ISBN: (纸本)9781538665657

In view of the application requirements for VAV in forest rescue and border patrol, this paper takes a single small VAV equipped with visual sensors as the research object. Combined the encoder and decoder structure in the field of deep learning, a deep coding algorithm for scene perception is put forward. Based on the autoencoder, the algorithm achieves the dimension reduction and scene reconstruction by increasing the number of layers in the coding network. Based on the ROS environment, this paper sets up a simulation experiment data acquisition system and verifies the effectiveness of the dimensionality reduction algorithm.

关键词： scene representation deep autoencoder UAV perception

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Self-Supervised Speech Enhancement for Arabic Speech Recognition in Real-World Environments

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TRAITEMENT DU SIGNAL 2021年第2期38卷 349-358页

作者： Dendani, Bilal Bahi, Halima Sari, Toufik Univ Badji Mokhtar Annaba Comp Sci Dept Annaba 23000 Algeria Univ Badji Mokhtar Annaba Labged Lab Annaba 23000 Algeria

Mobile speech recognition attracts much attention in the ubiquitous context, however, background noises, speech coding, and transmission errors are prone to corrupt the incoming speech. Therein, building a robust speech recognizer requires the availability of a large number of real-world speech samples. Arabic language, like many other languages, lacks such resources;to overcome this limitation, we propose a speech enhancement step, before the recognition begins. For the speech enhancement purpose, we suggest the use of a deep autoencoder (DAE) algorithm. A two-step procedure is suggested: in the first step, an overcomplete DAE is trained in an unsupervised way, and in the second one, a denoising DAE is trained in a supervised way leveraging the clean speech produced in the previous step. Experimental results performed on a real-life mobile database confirmed the potentials of the proposed approach and show a reduction of the WER (Word Error Rate) of a ubiquitous Arabic speech recognizer. Further experiments show an improvement of the perceptual evaluation of speech quality (PESQ), and the short-time objective intelligibility (STOI) as well.

关键词： Arabic language deep autoencoder deep learning self-supervised speech enhancement speech recognition ubiquitous systems

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CONTROL ENGINEERING PRACTICE 2021年 113卷 104843-104843页

作者： Guo, Zijian Shang, Chao Ye, Hao Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Automat Beijing 100084 Peoples R China

Establishing a quantitative similarity between different datasets has gained prevalence and significance in many applications of process control. In industrial practice, process data are usually multi-dimensional, nonlinearly correlated, and with unknown time-varying distribution, which raise immense challenge for reasonably evaluating similarity. To address this issue, a novel similarity metric based on deep autoencoder (DAE) and the Wasserstein distance is proposed in this paper. Specifically, DAE is used to first capture nonlinear relationship embedded in multivariate process data, and the reconstruction error acts as an indicator to reveal discrepancy between two datasets. After that, the similarity is characterized by evaluating the gap between reconstruction error distributions using Wasserstein distance. The proposed similarity metric has wide applicability in a variety of data analytics tasks including pattern matching, fault diagnosis and mode classifications. Both simulated data and industrial data collected from a real iron-making process are utilized to carry out comprehensive case studies. It is shown that the proposed similarity metric not only enjoys better rationality and sensitivity than generic similarity metrics, but also effectively improves the accuracy of fault diagnosis and mode classification based on big process data.

关键词： Similarity metric deep autoencoder Wasserstein distance Unsupervised mode classification Big process data

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A novel unsupervised anomaly detection method for rotating machinery based on memory augmented temporal convolutional autoencoder

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartB期123卷

作者： Li, Wanxiang Shang, Zhiwu Zhang, Jie Gao, Maosheng Qian, Shiqi Tiangong Univ Sch Mech Engn Tianjin 300387 Peoples R China Tianjin Modern Electromech Equipment Technol Key L Tianjin 300387 Peoples R China

During the operation of rotating machinery, the occurrence of unknown fault types makes it impossible for the artificial intelligence-based fault diagnosis model to distinguish. Furthermore, due to the excessive generalization capability of the autoencoder, the unsupervised anomaly detection method based on the autoencoder is difficult to effectively distinguish normal and abnormal samples. To address the above problem, this paper proposed an unsupervised anomaly detection method based on memory augmented temporal convolutional autoencoder (MATCAE). Firstly, a novel temporal convolutional autoencoder model is constructed based on dilated causal convolution, skip connection and autoencoder to facilitate the model to learn the temporal features of the input data, thereby enhancing the model's ability to capture the complex structure of the data. Then, a memory augmented module is designed using a memory matrix and an attention mechanism to expand the distribution interval between the reconstructed samples of normal and abnormal samples and reduce the sample capacity in the overlapping area. Finally, an anomaly detection module based on Euclidean distance, cosine distance and absolute mean square error is designed to improve the reliability of the metric between the input and reconstructed samples. To verify the effectiveness of the proposed method, experimental validation is carried out on a gearbox anomaly detection dataset. The experimental results show that the proposed method has higher anomaly detection accuracy and better noise robustness than other advanced anomaly detection methods, where the average performance metric is improved by 26.86% at the highest and 2.80% at the lowest.

关键词： Rotating machinery Anomaly detection Dilated convolutional deep autoencoder Unsupervised learning Memory augmented

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A hybrid approach for artwork recommendation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartD期126卷

作者： Gatti, Ignacio Diaz-Pace, J. Andres Schiaffino, Silvia UNCPBA CONICET ISISTAN Tandil Argentina

Museums usually exhibit thousands of artworks, and nowadays, they often have their collections online for visitors. In these collections, the curators are responsible for organizing the artworks seeking a delicate balance between emotion and reason. Given an initial artwork, however, a visitor is likely to select and admire a set of related artworks that match her interests. This setting can be seen as a recommendation problem in the art domain. Although image recommendation systems have been previously developed, considering the artwork nature is a fundamental aspect when designing a recommender system in this domain. Thus, we propose a hybrid recommendation approach that combines deep autoencoders with a social influence graph in order to capture the visual aspects and context of artworks (represented by images). These mechanisms inform the generation of rankings of related artworks. In this context, we report on a case study with a group of art experts who assessed the rankings of artworks recommended by our approach. Although preliminary, the results showed a better precision than traditional strategies based solely on image features or metadata. Furthermore, the recommendations exhibited diversity properties, avoiding typical over-specialization problems of content-based techniques.

关键词： Recommender systems Artwork deep autoencoder Ontology

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Two-step residual-error based approach for anomaly detection in engineering systems using variational autoencoders

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 101卷

作者： Gonzalez-Muniz, Ana Diaz, Ignacio Cuadrado, Abel A. Garcia-Perez, Diego Perez, Daniel Univ Oviedo Elect Engn Dept Edif Dept Oeste 2Campus Viesques S-N Gijon 33204 Spain Univ Leon Escuela Ingn SUPPRESS Res Grp Campus Vegazana Leon 24007 Spain

Anomaly detection is a crucial task in the engineering systems field. However, there is usually little or no information about all possible abnormal modes in systems. Hence, a common approach is to build a model of healthy behaviour, based on normal operation data, so that anomaly detection would depend on how well new data fit this model. According to this idea, we propose a residual-error based approach consisting of: a variational autoencoder, used to model the probability density function of the system's healthy behaviour;and a two-step classification algorithm, which classifies the incoming samples based on their residuals, and reports not only their normal/anomalous nature but also that of their components. We have tested this proposal in three different engineering contexts and we have compared its performance with that of state-of-the-art approaches, demonstrating its capability to successfully detect and characterize anomalies.

关键词： Anomaly detection Novelty detection deep autoencoder Variational autoencoder Engineering systems

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A White Shark Equilibrium Optimizer with a Hybrid deep-Learning-Based Cybersecurity Solution for a Smart City Environment

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SENSORS 2023年第17期23卷 7370-7370页

作者： Almuqren, Latifah Aljameel, Sumayh S. Alqahtani, Hamed Alotaibi, Saud S. Hamza, Manar Ahmed Salama, Ahmed S. Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol SAUDI ARAMCO Cybersecur Chair Comp Sci Dept POB 1982 Dammam 31441 Saudi Arabia King Khalid Univ Coll Comp Sci Dept Informat Syst Unit Cybersecur Abha 61421 Saudi Arabia Umm Al Qura Univ Coll Comp & Informat Syst Dept Informat Syst Mecca 24382 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Dept Comp & Self Dev Preparatory Year Deanship Al Kharj 16278 Saudi Arabia Future Univ Egypt Fac Engn & Technol Dept Elect Engn New Cairo 11845 Egypt

Smart grids (SGs) play a vital role in the smart city environment, which exploits digital technology, communication systems, and automation for effectively managing electricity generation, distribution, and consumption. SGs are a fundamental module of smart cities that purpose to leverage technology and data for enhancing the life quality for citizens and optimize resource consumption. The biggest challenge in dealing with SGs and smart cities is the potential for cyberattacks comprising Distributed Denial of Service (DDoS) attacks. DDoS attacks involve overwhelming a system with a huge volume of traffic, causing disruptions and potentially leading to service outages. Mitigating and detecting DDoS attacks in SGs is of great significance to ensuring their stability and reliability. Therefore, this study develops a new White Shark Equilibrium Optimizer with a Hybrid deep-Learning-based Cybersecurity Solution (WSEO-HDLCS) technique for a Smart City Environment. The goal of the WSEO-HDLCS technique is to recognize the presence of DDoS attacks, in order to ensure cybersecurity. In the presented WSEO-HDLCS technique, the high-dimensionality data problem can be resolved by the use of WSEO-based feature selection (WSEO-FS) approach. In addition, the WSEO-HDLCS technique employs a stacked deep autoencoder (SDAE) model for DDoS attack detection. Moreover, the gravitational search algorithm (GSA) is utilized for the optimal selection of the hyperparameters related to the SDAE model. The simulation outcome of the WSEO-HDLCS system is validated on the CICIDS-2017 dataset. The widespread simulation values highlighted the promising outcome of the WSEO-HDLCS methodology over existing methods.

关键词： smart grids DDoS attacks cybersecurity feature selection deep autoencoder smart cities

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