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StyleVC: Non-Parallel Voice Conversion with Adversarial Style Generalization 26

StyleVC: Non-Parallel Voice Conversion with Adversarial Styl...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Hwang, In-Sun Lee, Sang-Hoon Lee, Seong-Whan Korea Univ Dept Artificial Intelligence Seoul South Korea Korea Univ Dept Brain & Cognit Engn Seoul South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Voice conversion converts the voice while maintaining the language information. It uses two samples to synthesize speech: the source sample is used for content, the target sample is used for style representation. therefore, VC has been progressed to design information flow to disentangle content and style in a speech. However, separated representations are damaged while passing sparse subspace. Besides, VC models suffer from the training-inference mismatch problem: they only use one sample in training. Accordingly, the model extracts inappropriate content and style representation and generates low-quality speech during inference. To address the mismatch scenario problem, we propose a StyleVC, which utilizes adversarial style generalization. First, we propose style generalization, which captures global style representation and restricts the model from copying information. Second, we use a pitch predictor to estimate pitch information according to content and style representation. third, we further use adversarial training to make the model generate more realistic speech. Finally, we demonstrate our proposed model can generate high-quality speech. the experimental results also show that the proposed StyleVC significantly outperforms to extract the desired features and improve audio quality during inference.

关键词： Training Feature extraction pattern recognition Decoding

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Aspect-based Sentiment Analysis with Graph Convolutional Networks over Dependency Awareness 26

Aspect-based Sentiment Analysis with Graph Convolutional Net...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Wang, Xue Liu, Peiyu Zhu, Zhenfang Lu, Ran Shandong Normal Univ Jinan Shandong Peoples R China Shandong Jiaotong Univ Jinan Shandong Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarities of specific aspects in a comment sentence. Nowadays, models based on graph neural networks enhance semantic perception by using dependency relations on dependency graphs to analyze context and aspect words. However, these models ignore the importance of the dependency type information contained in word relations and do not utilize the dependency types to pay attention to semantic information and the noise problem caused by dependency tree parsing error. To solve the above problems, we propose a novel deep dependency-aware graph convolutional networks (DA-GCN) model in this paper. Among them, the DA-GCN establishes interactive relations with multi-head attention and makes use of the grammar information of dependency perception jointly to effectively learn related information from the generated graphs. We introduce multiple conditional random fields fusing structured attention to better capture specific aspect opinion words. Experimental results on five datasets prove the effectiveness and advancement of our proposed model.

关键词： Sentiment analysis Analytical models Semantics Benchmark testing Graph neural networks pattern recognition Grammar

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Simple Yet Effective Approach to Repetitive Behavior Classification based on Siamese Network 26

Simple Yet Effective Approach to Repetitive Behavior Classif...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Yoo, Cheol-Hwan Yoo, Jang-Hee Kim, Ho-Won Han, ByungOk Elect & Telecommun Res Inst ETRI Artificial Intelligence Res Lab Daejeon South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Conventional studies dealing with repetition detection have mainly focused on tasks of temporal localization or counting the number of repetitions in videos. However, direct discrimination between repetitive and non-repetitive behaviors in videos, called repetitive behavior classification (RBC), has attracted less attention despite its great potential and advantages of: 1) filling the demands in the fields such as classification of repetitive behaviors in children with autism spectrum disorder (ASD) and helping to alleviate manual and time-consuming diagnostic procedures, 2) directly learning representation of differences between repetition and non-repetition patterns along the temporal dimension, and 3) being an effective alternative to the existing repetition counting and temporal segmentation tasks that are struggling with insufficient data and laborious manual annotation effort. In this paper, to the best of our knowledge, we firstly cast the problem of the RBC using deep learning frameworks. For this, we propose a simple yet effective add-on network, SiRepNet, that exploits the Siamese network structure to learn the inherent properties of repetitive behaviors. We also composed the RBC dataset by re-purposing and re-organizing Kinetics and Countix datasets for training our method. To validate our ideas, we carried out extensive experiments on RBC datasets, which showed performance improvement over state-of-the-art video classification algorithms by simply attaching our scheme to them for the RBC task.

关键词： Training Location awareness Smoothing methods Manuals Behavioral sciences Classification algorithms pattern recognition

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Improving Persona Understanding for Persona-based Dialogue Generation with Diverse Knowledge Selection 26

Improving Persona Understanding for Persona-based Dialogue G...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Wang, Yuanying Xu, Fuyong Wang, Ru Zhu, Zhenfang Liu, Peiyu Lu, Ran Shandong Normal Univ Sch Informat Sci & Engn Jinan Peoples R China Shandong Jiaotong Univ Sch Informat Sci & Elect Engn Jinan Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

A significant goal in an open-domain dialogue system is to make chatbots generate more persona coherent responses given a context. To achieve this goal, some researchers attempt to introduce persona information into neural dialogue models. However, these neural dialogue models describe excessively persona traits during the conversation, which still suffer from the problem of generating boring and meaningful responses. In this paper, we divide the open-domain personalized dialogue generation task into two processes, persona recognition and persona fusion. In the persona recognition process, we use the pretraining model to encode the personas and conversation history independently, which is beneficial to the persona information fusion. then, we design a dynamic persona fusion mechanism to effectively mine the relevance of dialogue context and persona information, and dynamically predict whether to incorporate persona features in the process of the dialogues. Our model outperforms with 1.23% in Acc., 0.36% in BLEU, 0.92% in F1, and 0.036% in Distinct than baseline models. the experimental results on the ConvAI2 dataset illustrate that the proposed model is superior to baseline approaches for generating more coherent and persona consistent responses.

关键词： Oral communication Predictive models Chatbots pattern recognition History Boring Task analysis

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Artificial Intelligence for the Classification of Neuromuscular Diseases Using Dominant MUAP 8

Artificial Intelligence for the Classification of Neuromuscu...

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8th international conference on Smart Structures and Systems, ICSSS 2022

作者： Maheshwary, Priti Vinu, W. Velvadivu, P. Shukla, Surendra Kumar Srivastava, Prabhat Kumar Pareek, Prakash Centre for Internet of Things and Advance Computing Rabindranath Tagore University Bhopal India Annamalai University Department of Physical Education Tamilnadu Chidambaram India Coimbatore Institute of Technology Department of Computing-Data Science Tamilnadu Coimbatore India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Quantum University Department of Computer Science and Engineering Uttrakhand Roorkee India Vishnu Institute of Technology Department of Electronics and Communication Engineering Andhra Pradesh Bhimavaram India

ISBN: (纸本)9781665497619

Motor-unit-action-potentials in an electromyographic isolated signals shape & sounds are valuable diagnosis information for neuro-muscular disorders-treatment & management. An expert can analyse these parameters qualitatively or quantitative by pattern recognition techniques. Because of benefits of the quantitatively method of EMG, producing robust automated MUAP types is investigated, & many systems is produced for this purpose, but correctness of the previous methods isn't peak enough to use in clinical settings. the developed system uses an EMG signal decomposition mechanism to extract both time & frequency domain for M-U-A-P's retrieved from E-M-G. Un-similar type of algorithms was studied, including 1 & many types with many sub-sets characteristics. the multi-classifier methods proposed here performed well in experiments by real set EMG. the multi-classifier, which aggregates base classifiers using multiple feature sets & both trainable & non-trainable fusion techniques, performed the best of the methods studied, with an average precision is 98%. © 2022 IEEE.

关键词： Support vector machines Time-frequency analysis Shape Neuromuscular Morphology Electromyography pattern recognition

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DenseGAP: Graph-Structured Dense Correspondence Learning with Anchor Points 26

DenseGAP: Graph-Structured Dense Correspondence Learning wit...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Kuang, Zhengfei Li, Jiaman He, Mingming Wang, Tong Zhao, Yajie Univ Southern Calif Los Angeles CA USA USC Inst Creat Technol Los Angeles CA USA Stanford Univ Stanford CA USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Establishing dense correspondence between two images is a fundamental computer vision problem, which is typically tackled by matching local feature descriptors. However, without global awareness, such local features are often insufficient for disambiguating similar regions. And computing the pairwise feature correlation across images is both computation-expensive and memory-intensive. To make the local features aware of the global context and improve their matching accuracy, we introduce DenseGAP, a new solution for efficient Dense correspondence learning with a Graph-structured neural network conditioned on Anchor Points. Specifically, we first propose a graph structure that utilizes anchor points to provide sparse but reliable prior on inter- and intra-image context and propagates them to all image points via directed edges. We also design a graph-structured network to broadcast multi-level contexts via light-weighted message-passing layers and generate high-resolution feature maps at low memory cost. Finally, based on the predicted feature maps, we introduce a coarse-to-fine framework for accurate correspondence prediction using cycle consistency. Our feature descriptors capture both local and global information, thus enabling a continuous feature field for querying arbitrary points at high resolution. through comprehensive ablative experiments and evaluations on large-scale indoor and outdoor datasets, we demonstrate that our method advances the state-of-the-art of correspondence learning on most benchmarks.

关键词： Computer vision Costs Correlation Image edge detection Neural networks Estimation pattern recognition

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Spatial Urban Change and Trend Analysis of Davanagere City Boundary Between 2000 - 2020 8

Spatial Urban Change and Trend Analysis of Davanagere City B...

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8th IEEE international conference on Distributed computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Shivakumar, B.R. Pallavi, M. Reza, Sayed Mohsin NMAM Institute of Technology Nitte Dept. of ECE Udupi India Manipal Institute of Technology Manipal Academy of Higher Education Department of Aeronautical and Automobile Engineering Manipal576104 India Pennsylvania State University Department of Computer Science Harrisburg Pike HarrisburgPA United States

ISBN: (纸本)9798350350593

India is one of the world's fastest-growing economic engines, however, this rapid urbanization has a major impact on the country. A few cities on the Indian subcontinent have grown rapidly in the past two decades to become major economic hubs. Since these commercial centers will soon make significant contributions to the economic development of the nation, it is crucial to study the effects of urbanization and the patterns of urbanization in these cities. Despite the fact that much work has been conducted on urbanization and its effects, the vast maj ority of these works concentrated on megacities at the global scale. though Karnataka, India, is home to some of the fastest-growing urban cities in the world, there is a lack of documented research on the subject. the purpose of this article is to examine the last 20 years of urban change within the Davanagere city limits, in terms of both urban trend change and urban change detection. through periodic change analysis over a five-year period, we investigate the urban change patterns in the study area from 2000 to 2020. Change analysis is accomplished by evaluating the rate of change in land use and land cover at predetermined distances from the city center, and the direction of urban change is determined by defining the direction of urban change. the study has a strong emphasis on assessing the effects of urbanization on the two major water bodies that are located within the city limits. Findings show that urbanization in the study area increased by 213.45 % between 2000 and 2020. In addition, the findings show that the study area is growing in size and moving westward, with minimal effect on the water bodies in the issue. these findings have a wide range of applications for urban planners, managers, and academics, from analyzing urban growth to keeping tabs on and controlling changes within a city's infrastructure. © 2024 IEEE.

关键词： classification remote sensing urban change urban change pattern urban trend analysis urbanization

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Deep Residual Network with Multi-Image Attention for Imputing Under Clouds in Satellite Imagery 26

Deep Residual Network with Multi-Image Attention for Imputin...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Yang, Xian Zhao, Yifan Vatsavai, Ranga Raju North Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA BReLL Lirio Knoxville TN USA North Carolina State Univ CGA CSC Raleigh NC 27695 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Earth observations from remote sensing imagery play an important role in many environmental applications ranging from natural resource (e.g., crops, forests) monitoring to man-made object (e.g., builds, factories) recognition. Most widely used optical remote sensing data however is often contaminated by clouds making it hard to identify the objects underneath. Fortunately, with the recent advances and increased operational satellites, the spatial and temporal density of image collections have significantly increased. In this paper, we present a novel deep learning-based imputation technique for inferring spectral values under the clouds using nearby cloud-free satellite image observations. the proposed deep learning architecture, extended contextual attention (ECA), exploits similar properties from the cloud-free areas to tackle clouds of different sizes occurring at arbitrary locations in the image. A contextual attention mechanism is incorporated to utilize the useful cloud-free information from multiple images. To maximize the imputation performance of the model on the cloudy patches instead of the entire image, a two-phase custom loss function is deployed to guide the model. To study the performance of our model, we trained our model on a benchmark Sentinel-2 dataset by superimposing real-world cloud patterns. Extensive experiments and comparisons against the state-of-the-art methods using pixel-wise and structural metrics show the improved performance of our model. Our experiments demonstrated that the ECA method is consistently better than all other methods, it is 28.4% better on MSE and 31.7% better on cloudy MSE as compared to the state-of-the-art EDSR network.

关键词： Earth Deep learning Cloud computing Satellites Clouds Benchmark testing Data models

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Cross-Guided Feature Fusion with Intra-Modality Reweighting for Multi-Spectral Pedestrian Detection 26

Cross-Guided Feature Fusion with Intra-Modality Reweighting ...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Deng, Jian Li, Mingyue Chen, Yongxin Shao, Zhenzhou Qu, Ying Guan, Yong Zhang, Jun Shi, Zhiping Capital Normal Univ Coll Informat Engn Beijing Peoples R China Beijing Key Lab Light Ind Robot & Safety Verifica Beijing Peoples R China Beijing Adv Innovat Ctr Imaging Technol Beijing Peoples R China Capital Normal Univ Sch Math Sci Beijing Peoples R China Univ Tennessee Engn Coll Knoxville TN USA Beijing Smartchip Microelect Technol Co Ltd Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Multi-spectral pedestrian detection has gained extensive attention over the past decade. To alleviate the problem of modality imbalance in the multi-spectral tasks, a novel cross-guided feature fusion network based on the auto-encoder framework is proposed using RGB-thermal image pairs as inputs. To obtain the complementary features, a cross-guided loss is designed, so that the output images are balanced with both modalities in an unsupervised manner. An intra-modality reweighting module is implemented to filter the redundant features before the fusion. Finally, YOLOv3 is chosen as the detector fed by the fused features. the proposed method is verified using the public KAIST and VOT-RGBT datasets. Experimental results demonstrate that the proposed method can outperform the state-of-the-art methods, the miss rate of pedestrian detection reaches 48.57% and 4.52% using KAIST and VOT-RGBT datasets, respectively.

关键词： Redundancy Detectors Feature extraction pattern recognition Task analysis

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Adversarial Attack on Semantic Segmentation Preprocessed with Super Resolution 26

Adversarial Attack on Semantic Segmentation Preprocessed wit...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Lim, Gyeongsup Kim, Minjae Hur, Junbeom Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Computer vision tasks, such as image classification, semantic segmentation, and super resolution, are broadly utilized in many applications. Recent studies revealed that machine learning-based models for the computer vision tasks are vulnerable to adversarial attacks. Since the adversarial attack can disturb the computer vision models in real-world systems, many countermeasures have been proposed against the adversarial attacks, such as denoising, resizing, and machine learning-based super resolution model as a preprocessing. Recently, a prior work demonstrated that the super resolution model as a preprocessing can be vulnerable to the adversarial attack targeted to the preprocessing itself, only when the perturbation is inactive before the preprocessing. However, we also found that the perturbation before the preprocessing can be another serious threat if the super resolution model is used for a mitigation of adversarial attacks. In this paper, we propose Layered Adversary Generation (LAG) that generates the adversarial example by recursively injecting noises to clean image in white-box environment. We then show that LAG is effective to attack a semantic segmentation model even if the super resolution models with/without two countermeasures as auxiliary methods such as resizing and denoising are adopted to mitigate the adversarial attacks. Furthermore, we demonstrate that LAG is transferable across other super resolution models. Lastly, we discuss our attack method in gray-box and black-box environments, and suggests a mitigation for robust preprocessing.

关键词： Computer vision Computational modeling Semantic segmentation Perturbation methods Superresolution Noise reduction pattern recognition

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