Deep Feature Learning And Adaptation For Computer Vision
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Deep Feature Learning And Adaptation For Computer Vision
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Author : Abu Md Niamul Taufique
language : en
Publisher:
Release Date : 2022
Deep Feature Learning And Adaptation For Computer Vision written by Abu Md Niamul Taufique and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Computer vision categories.
"We are living in times when a revolution of deep learning is taking place. In general, deep learning models have a backbone that extracts features from the input data followed by task-specific layers, e.g. for classification. This dissertation proposes various deep feature extraction and adaptation methods to improve task-specific learning, such as visual re-identification, tracking, and domain adaptation. The vehicle re-identification (VRID) task requires identifying a given vehicle among a set of vehicles under variations in viewpoint, illumination, partial occlusion, and background clutter. We propose a novel local graph aggregation module for feature extraction to improve VRID performance. We also utilize a class-balanced loss to compensate for the unbalanced class distribution in the training dataset. Overall, our framework achieves state-of-the-art (SOTA) performance in multiple VRID benchmarks. We further extend our VRID method for visual object tracking under occlusion conditions. We motivate visual object tracking from aerial platforms by conducting a benchmarking of tracking methods on aerial datasets. Our study reveals that the current techniques have limited capabilities to re-identify objects when fully occluded or out of view. The Siamese network based trackers perform well compared to others in overall tracking performance. We utilize our VRID work in visual object tracking and propose Siam-ReID, a novel tracking method using a Siamese network and VRID technique. In another approach, we propose SiamGauss, a novel Siamese network with a Gaussian Head for improved confuser suppression and real time performance. Our approach achieves SOTA performance on aerial visual object tracking datasets. A related area of research is developing deep learning based domain adaptation techniques. We propose continual unsupervised domain adaptation, a novel paradigm for domain adaptation in data constrained environments. We show that existing works fail to generalize when the target domain data are acquired in small batches. We propose to use a buffer to store samples that are previously seen by the network and a novel loss function to improve the performance of continual domain adaptation. We further extend our continual unsupervised domain adaptation research for gradually varying domains. Our method outperforms several SOTA methods even though they have the entire domain data available during adaptation."--Abstract.
Domain Adaptation In Computer Vision With Deep Learning
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Author : Hemanth Venkateswara
language : en
Publisher: Springer Nature
Release Date : 2020-08-18
Domain Adaptation In Computer Vision With Deep Learning written by Hemanth Venkateswara and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-18 with Computers categories.
This book provides a survey of deep learning approaches to domain adaptation in computer vision. It gives the reader an overview of the state-of-the-art research in deep learning based domain adaptation. This book also discusses the various approaches to deep learning based domain adaptation in recent years. It outlines the importance of domain adaptation for the advancement of computer vision, consolidates the research in the area and provides the reader with promising directions for future research in domain adaptation. Divided into four parts, the first part of this book begins with an introduction to domain adaptation, which outlines the problem statement, the role of domain adaptation and the motivation for research in this area. It includes a chapter outlining pre-deep learning era domain adaptation techniques. The second part of this book highlights feature alignment based approaches to domain adaptation. The third part of this book outlines image alignment procedures for domain adaptation. The final section of this book presents novel directions for research in domain adaptation. This book targets researchers working in artificial intelligence, machine learning, deep learning and computer vision. Industry professionals and entrepreneurs seeking to adopt deep learning into their applications will also be interested in this book.
Pattern Recognition And Computer Vision
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Author : Jian-Huang Lai
language : en
Publisher: Springer
Release Date : 2018-11-01
Pattern Recognition And Computer Vision written by Jian-Huang Lai and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-01 with Computers categories.
The four-volume set LNCS 11256, 11257, 11258 and 11259 constitutes the refereed proceedings of the First Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018, held in Guangzhou, China, in November 2018. The 179 revised full papers presented were carefully reviewed and selected from 399 submissions. The papers have been organized in the following topical sections: Part I: Biometrics, Computer Vision Application. Part II: Deep Learning. Part III: Document Analysis, Face Recognition and Analysis, Feature Extraction and Selection, Machine Learning. Part IV: Object Detection and Tracking, Performance Evaluation and Database, Remote Sensing.
Deep Learning For Data Mining Unsupervised Feature Learning And Representation
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Author : Mr. Srinivas Rao Adabala
language : en
Publisher: Xoffencerpublication
Release Date : 2023-08-14
Deep Learning For Data Mining Unsupervised Feature Learning And Representation written by Mr. Srinivas Rao Adabala and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-14 with Computers categories.
Deep learning has developed as a useful approach for data mining tasks such as unsupervised feature learning and representation. This is thanks to its ability to learn from examples with no prior guidance. Unsupervised learning is the process of discovering patterns and structures in unlabeled data without the use of any explicit labels or annotations. This type of learning does not require the data to be annotated or labelled. This is especially helpful in situations in which labelled data are few or nonexistent. Unsupervised feature learning and representation have seen widespread application of deep learning methods such as auto encoders and generative adversarial networks (GANs). These algorithms learn to describe the data in a hierarchical fashion, where higher-level characteristics are stacked upon lower-level ones, capturing increasingly complicated and abstract patterns as they progress. Neural networks are known as Auto encoders, and they are designed to reconstruct their input data from a compressed representation known as the latent space. The hidden layers of the network are able to learn to encode valuable characteristics that capture the underlying structure of the data when an auto encoder is trained on input that does not have labels attached to it. It is possible to use the reconstruction error as a measurement of how well the auto encoder has learned to represent the data. GANs are made up of two different types of networks: a generator network and a discriminator network. While the discriminator network is taught to differentiate between real and synthetic data, the generator network is taught to generate synthetic data samples that are an accurate representation of the real data. By going through an adversarial training process, both the generator and the discriminator are able to improve their skills. The generator is able to produce more realistic samples, and the discriminator is better able to tell the difference between real and fake samples. One meaningful representation of the data could be understood as being contained within the latent space of the generator. After the deep learning model has learned a reliable representation of the data, it can be put to use for a variety of data mining activities.
Proceedings Of International Conference On Machine Intelligence And Data Science Applications
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Author : Manish Prateek
language : en
Publisher: Springer Nature
Release Date : 2021-05-07
Proceedings Of International Conference On Machine Intelligence And Data Science Applications written by Manish Prateek and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-07 with Technology & Engineering categories.
This book is a compilation of peer-reviewed papers presented at the International Conference on Machine Intelligence and Data Science Applications, organized by the School of Computer Science, University of Petroleum & Energy Studies, Dehradun, on September 4 and 5, 2020. The book starts by addressing the algorithmic aspect of machine intelligence which includes the framework and optimization of various states of algorithms. Variety of papers related to wide applications in various fields like image processing, natural language processing, computer vision, sentiment analysis, and speech and gesture analysis have been included with upfront details. The book concludes with interdisciplinary applications like legal, health care, smart society, cyber physical system and smart agriculture. The book is a good reference for computer science engineers, lecturers/researchers in machine intelligence discipline and engineering graduates.
Advanced Methods And Deep Learning In Computer Vision
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Author : E. R. Davies
language : en
Publisher: Academic Press
Release Date : 2021-11-09
Advanced Methods And Deep Learning In Computer Vision written by E. R. Davies and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-09 with Technology & Engineering categories.
Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5–10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection. This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students. - Provides an important reference on deep learning and advanced computer methods that was created by leaders in the field - Illustrates principles with modern, real-world applications - Suitable for self-learning or as a text for graduate courses
Ecai 2020
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Author : G. De Giacomo
language : en
Publisher: IOS Press
Release Date : 2020-09-11
Ecai 2020 written by G. De Giacomo and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-11 with Computers categories.
This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of more than 1,700 submissions was received for ECAI 2020, of which 1,443 were reviewed. Of these, 361 full-papers and 36 highlight papers were accepted (an acceptance rate of 25% for full-papers and 45% for highlight papers). The book is divided into three sections: ECAI full papers; ECAI highlight papers; and PAIS papers. The topics of these papers cover all aspects of AI, including Agent-based and Multi-agent Systems; Computational Intelligence; Constraints and Satisfiability; Games and Virtual Environments; Heuristic Search; Human Aspects in AI; Information Retrieval and Filtering; Knowledge Representation and Reasoning; Machine Learning; Multidisciplinary Topics and Applications; Natural Language Processing; Planning and Scheduling; Robotics; Safe, Explainable, and Trustworthy AI; Semantic Technologies; Uncertainty in AI; and Vision. The book will be of interest to all those whose work involves the use of AI technology.
Visual Domain Adaptation In The Deep Learning Era
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Author : Gabriela Csurka
language : en
Publisher: Springer Nature
Release Date : 2022-06-06
Visual Domain Adaptation In The Deep Learning Era written by Gabriela Csurka and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-06 with Computers categories.
Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance. While in many situations huge volumes of unlabeled data can be and often are generated and available, the cost of acquiring data labels remains high. Transfer learning (TL), and in particular domain adaptation (DA), has emerged as an effective solution to overcome the burden of annotation, exploiting the unlabeled data available from the target domain together with labeled data or pre-trained models from similar, yet different source domains. The aim of this book is to provide an overview of such DA/TL methods applied to computer vision, a field whose popularity has increased significantly in the last few years. We set the stage by revisiting the theoretical background and some of the historical shallow methods before discussing and comparing different domain adaptation strategies that exploit deep architectures for visual recognition. We introduce the space of self-training-based methods that draw inspiration from the related fields of deep semi-supervised and self-supervised learning in solving the deep domain adaptation. Going beyond the classic domain adaptation problem, we then explore the rich space of problem settings that arise when applying domain adaptation in practice such as partial or open-set DA, where source and target data categories do not fully overlap, continuous DA where the target data comes as a stream, and so on. We next consider the least restrictive setting of domain generalization (DG), as an extreme case where neither labeled nor unlabeled target data are available during training. Finally, we close by considering the emerging area of learning-to-learn and how it can be applied to further improve existing approaches to cross domain learning problems such as DA and DG.
Feature Learning And Understanding
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Author : Haitao Zhao
language : en
Publisher: Springer Nature
Release Date : 2020-04-03
Feature Learning And Understanding written by Haitao Zhao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-03 with Science categories.
This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence.
Pattern Recognition And Computer Vision
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Author : Zhouchen Lin
language : en
Publisher: Springer Nature
Release Date : 2024-11-02
Pattern Recognition And Computer Vision written by Zhouchen Lin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-02 with Computers categories.
This 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 18–20, 2024. The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications.