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A Deep Learning Study On Understanding Banglish and Abbreviated Words Used in Social Media

A Deep Learning Study On Understanding Banglish and Abbrevia...

引用

international conference on ent computing and Control Systems (ICICCS)

作者： Suharto Das Md Sanzidul Islam Imran Mahmud Daffodil International University Dhaka Bangladesh

Day by Day, the trend of using social media(SM) has increased among the people. With the increasing rate, the use of Banglish(Merge of Bangla and English) and shortcut words has also being increased. In this research, Banglish and Shortcut words have been fully converted to English. For this type of conversation, we have used the CNNs method and the method consist of multiple layer and these layers are connected to each other. This method is considered to be the best method because it does not need any feature extraction. The convolutional neuron network is used in many areas such as image and pattern recognition, speech recognition, natural language processing and video analysis. In this research we have used CNN method because we have decided to use computer vision as some of the words are close to each other and at first we need to convert all the shortcut words into images for pre-processing the data. With the help of CNN method searched for the Banglish and shortcut words that people use for their daily conversation. At first we find the different representative of a single word and then we converted those shortcut and Banglish words to main word using CNNs method.

关键词： Deep learning Computer vision Social networking (online) Neurons Speech recognition Market research Feature extraction

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A General Theory of Adaptivity and Homeostasis in the Brain and in the Body

A General Theory of Adaptivity and Homeostasis in the Brain ...

引用

IEEE international conference on Cognitive Informatics

作者： Bernard Widrow Dept. of Packard Electrical Engineering Stanford University Stanford CA

ISBN: (纸本)9781665421201

Hebbian learning is widely accepted in the fields of psychology, neurology, and neurobiology. It is one of the fundamental premises of neuroscience. The LMS (least mean square) algorithm of Widrow and Hoff is the world’s most widely used adaptive algorithm, fundamental in the fields of signal processing, control systems, communication systems, pattern recognition, and artificial neural networks. These learning paradigms are very different. Hebbian learning is unsupervised. LMS learning is supervised. However, a form of LMS can be constructed to perform unsupervised learning and, as such, LMS can be used in a natural way to implement Hebbian learning. Combining the two paradigms creates a new unsupervised learning algorithm, Hebbian-LMS. This algorithm has practical engineering applications and provides insight into learning in living neural networks. A fundamental question is, how does learning take place in living neural networks? "Nature’s little secret," the learning algorithm practiced by nature at the neuron and synapse level, may well be the Hebbian-LMS algorithm.

关键词： Neuroscience Neurons Signal processing algorithms Psychology Signal processing pattern recognition Hebbian theory

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A Comparative Study on Autism Spectrum Disorder Detection via 3D Convolutional Neural Networks 25th

A Comparative Study on Autism Spectrum Disorder Detection vi...

引用

25th international conference on pattern recognition Workshops, ICPR 2020

作者： Zhang, Kaijie Wang, Wei Guo, Yijun Shan, Caifeng Wang, Liang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Beijing China Center for Excellence in Brain Science and Intelligence Technology Institute of Automation Chinese Academy of Sciences Beijing China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Artificial Intelligence Research Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030687625

The prevalence of Autism Spectrum Disorder (ASD) in the United States has increased by 178% from 2000 to 2016. However, due to the lack of well-trained specialists and the time-consuming diagnostic process, many children are not able to be promptly diagnosed. Recently, several research have taken steps to explore automatic video-based ASD detection systems with the help of machine learning and deep learning models, such as support vector machine (SVM) and long short-term memory (LSTM) model. However, the models mentioned above could not extract effective features directly from raw videos. In this study, we aim to take advantages of 3D convolution-based deep learning models to aid video-based ASD detection. We explore three representative 3D convolutional neural networks (CNNs), including C3D, I3D and 3D ResNet. In addition, a new 3D convolutional model, called 3D ResNeSt, is also proposed based on ResNeSt. We evaluate these models on an ASD detection dataset. The experimental results show that, on average, all of the four 3D convolutional models can obtain competitive results when compared to the baseline using LSTM model. Our proposed 3D ResNeSt model achieves the best performance, which improves the average detection accuracy from 0.72 to 0.85. © 2021, Springer Nature Switzerland AG.

关键词： Convolution

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On the Robustness of 3D Human Pose Estimation

On the Robustness of 3D Human Pose Estimation

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international conference on pattern recognition

作者： Zerui Chen Yan Huang Liang Wang School of Artificial Intelligence University of Chinese Academy of Sciences Center for Research on Intelligent Perception and Computing NLPR CASIA Chinese Academy of Sciences Artificial Intelligence Research (CAS-AIR)

It is widely shown that Convolutional Neural Networks (CNNs) are vulnerable to adversarial examples on most recognition tasks, such as image classification and segmentation. However, few work studies the more complicated task - 3D human pose estimation. This task often requires large-scale datasets, specialized network architectures, and it can be solved either from single-view RGB images or from multi-view RGB images. In this paper, we make the first attempt to investigate the robustness of current state-of-the-art 3D human pose estimation methods. To this end, we build four representative baseline models, where most of the current methods can be generally classified as one of them. Furthermore, we design targeted adversarial attacks to detect whether 3D pose estimators are robust to different camera parameters. For different types of methods, we present a comprehensive study of their robustness on the large-scale Human3.6M benchmark. Our work shows that different methods vary significantly in their resistance to adversarial attacks. Through extensive experiments, we show that multi-view 3D pose estimators can be more vulnerable to adversarial examples. We believe that our efforts can shed light on future works to design more robust 3D human pose estimators.

关键词： Image segmentation Three-dimensional displays Image recognition Pose estimation Network architecture Robustness pattern recognition

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Multi-label Contrastive Focal Loss for Pedestrian Attribute recognition

Multi-label Contrastive Focal Loss for Pedestrian Attribute ...

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international conference on pattern recognition

作者： Xiaoqiang Zheng Zhenxia Yu Lin Chen Fan Zhu Shilong Wang Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China School of computer science Chengdu University Of Information Technology Chengdu China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

Pedestrian Attribute recognition (PAR) has received extensive attention during the past few years. With the advances of deep convolutional neural networks (CNNs), the performance of PAR has been significantly improved. Existing methods tend to acquire attribute-specific features by designing various complex network structures with additional modules. Such additional modules, however, dramatically increase the number of network parameters. Meanwhile, the problems of class imbalance and hard attribute retrieving remain underestimated in PAR. In this paper, we explore the optimization mechanism of the training processing to account for these problems and propose a new loss function called Multi-label Contrastive Focal Loss (MCFL). This proposed MCFL emphasizes the hard and minority attributes by using a separated re-weighting mechanism for different positive and negative classes to alleviate the impact of the imbalance. MCFL is also able to enlarge the gaps between the intra-class of multi-label attributes, to force CNNs to extract more subtle discriminative features. We evaluate the proposed MCFL on three large public pedestrian datasets, including RAP, PA-100K, and PETA. The experimental results indicate that the proposed MCFL with the ResNet-50 backbone is able to outperform other state-of-the-art approaches in term of mean accuracy.

关键词： Training Visualization Force Complex networks Feature extraction pattern recognition Convolutional neural networks

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Peregrine Preying pattern-Based Differential Evolution 3rd

Peregrine Preying Pattern-Based Differential Evolution

引用

3rd international conference on soft computing: Theories and Applications, SoCTA 2018

作者： Jain, Sanjay Sharma, Vivek Kumar Kumar, Sandeep Amity University Rajasthan Jaipur India Jagannath University JaipurRajasthan India

ISBN: (纸本)9789811507502

The differential evaluation (DE) algorithm is an evolutionary algorithm. It is a popular metaheuristics that efficiently solved various complex optimization problems. This paper proposed modification in DE, motivated by the flying pattern of peregrine falcon while preying and named as peregrine preying pattern-based DE algorithm (P3DE). In P3DE, the flying pattern of peregrine ameliorate the exploration potential, while preserving the novel exploitation potential of the DE. The competence, precision, robustness and trustworthiness of the projected P3DE algorithm is analyzed while simulating it over 12 composite benchmark functions of diverse characteristics. The results of P3DE compared with DE and artificial bee colony algorithm to prove its competitiveness for selected problems. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Evolutionary algorithms

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Multi-task learning with deep dual-path network for facial attribute recognition 9

Multi-task learning with deep dual-path network for facial a...

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9th international conference on computing and pattern recognition, ICCPR 2020

作者： Lai, Xinyu Chen, Si Wang, Da-Han Zhu, Shunzhi School of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding China

ISBN: (纸本)9781450387835

Facial attribute recognition is a popular and challenging research topic in computer vision. In the traditional deep learning based attribute recognition methods, the mid-level network features and the differences between attribute groups are not fully explored. To solve the above problem, a deep dual-path network is proposed for facial attribute recognition. In the multi-task learning framework, two sub-networks are employed to respectively extract the features of two attribute groups, i.e., local attributes and global ones, and designed with both different scale images and different depth networks. Furthermore, an adaptive Focal loss penalty scheme is developed to automatically assign weights to handle the class imbalance problem for facial attribute recognition. Experimental results on the challenging CelebA dataset show that the proposed method achieves the better performance than state-of-the-art methods. © 2020 ACM.

关键词： Deep learning

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Recognizing soft Biometric on Pedestrian Using Convolutional Neural Network

Recognizing Soft Biometric on Pedestrian Using Convolutional...

引用

2020 international conference on Data Science and Its Applications, ICoDSA 2020

作者： Siswoyo, Tri Broto Arifianto, Anditya Ramadhani, Kutniawan Nur Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9781728182353

Video surveillance is very important in automatic surveillance. It is usually used to monitor any criminal activity and help to find the perpetrator. In the search for individual among pedestrians when the face image can no longer be recognized, then what can be recognized is the soft-biometric or attribute appears on the pedestrian. The attributes can be in the form of clothes or accessories worn by pedestrians. However, recognizing attributes is a more challenging task as the number of variations of the attribute is increasing. Therefore, there is a need of a system that can effectively recognize the soft-biometric of pedestrians that are expected to help in video surveillance. In this study, we propose a pedestrian soft-biometric recognition system that was built using the Convolutional Neural Network (CNN) method so that it can search for pedestrians wearing various attributes. In recognizing pedestrian soft-biometric, the authors conducted a study using transfer learning from the Resnet50 architecture. As this case belongs to the multi-label classification case, we also observed several scenarios for determining the threshold to better classify the appearance of attributes. Our system able to recognize 35 different attributes with the best results of this research indicate the average accuracy of 90.82% with 80.84% of precision and 81.08% of recall using test data. © 2020 IEEE.

关键词： Computer vision

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Graph Structure Learning Boosted Neural Network for Image Segmentation

Graph Structure Learning Boosted Neural Network for Image Se...

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IEEE conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Jinde Liu Zhang Zhang University of Chinese Academy of Sciences Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences

ISBN: (数字)9781665463829

ISBN: (纸本)9781665463836

Although Convolutional Neural Networks have made significant progress in image segmentation, it remains inadequate for exploring the structural relationships between image components and how graphs can be employed to guide image segmentation. To explore the structural relationships inherent in image components, the Graph Structure Learning Boosted Neural Network was proposed, which takes the contextual information generated by the CNN as features of the nodes and then uses a self-supervised graph generator to generate an adjacency matrix representing the image components connectivity. Then a Graph Neural Network (GNN) uses the adjacency matrix to fuse information between components according to their connectivity, thus transforming the CNN’s pixel classification problem into the GNN’s pixel classification problem. The whole model is lightweight and scalable, and extensive experiments have demonstrated the scalability of the model alongside the effectiveness of the method.

关键词： Image segmentation Fuses Scalability Surveillance Graph neural networks Generators Convolutional neural networks

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Digital Image Forensics Using Local Optimal-Oriented pattern and ELM 3rd

Digital Image Forensics Using Local Optimal-Oriented Pattern...

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3rd international conference on soft computing: Theories and Applications, SoCTA 2018

作者： Sharma, Surbhi Ghanekar, Umesh Kurukshetra136119 India

ISBN: (纸本)9789811507502

Due to the advances in computer graphic technology, it has become very difficult to classify computer graphics from photographic images. It is also very difficult to distinguish tampered images from authentic images. In the chapter, an algorithm has been proposed to solve both the problems simultaneously. A texture descriptor local optimal-oriented pattern has been used to extract features from computer-generated and photographic images. The extreme learning machine has been used to classify the images. Experiment results demonstrate that it has high accuracy and is also robust against post-processing operations. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Computer graphics

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