Domain Adaptation In Computer Vision With Deep Learning
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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.
Visual Domain Adaptation In The Deep Learning Era
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Author : Gabriela Csurka
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2022-04-05
Visual Domain Adaptation In The Deep Learning Era written by Gabriela Csurka and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-05 with Computers categories.
Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance/b>. 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.
Domain Adaptation In Computer Vision Applications
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Author : Gabriela Csurka
language : en
Publisher: Springer
Release Date : 2017-09-10
Domain Adaptation In Computer Vision Applications written by Gabriela Csurka and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-10 with Computers categories.
This comprehensive text/reference presents a broad review of diverse domain adaptation (DA) methods for machine learning, with a focus on solutions for visual applications. The book collects together solutions and perspectives proposed by an international selection of pre-eminent experts in the field, addressing not only classical image categorization, but also other computer vision tasks such as detection, segmentation and visual attributes. Topics and features: surveys the complete field of visual DA, including shallow methods designed for homogeneous and heterogeneous data as well as deep architectures; presents a positioning of the dataset bias in the CNN-based feature arena; proposes detailed analyses of popular shallow methods that addresses landmark data selection, kernel embedding, feature alignment, joint feature transformation and classifier adaptation, or the case of limited access to the source data; discusses more recent deep DA methods, including discrepancy-based adaptation networks and adversarial discriminative DA models; addresses domain adaptation problems beyond image categorization, such as a Fisher encoding adaptation for vehicle re-identification, semantic segmentation and detection trained on synthetic images, and domain generalization for semantic part detection; describes a multi-source domain generalization technique for visual attributes and a unifying framework for multi-domain and multi-task learning. This authoritative volume will be of great interest to a broad audience ranging from researchers and practitioners, to students involved in computer vision, pattern recognition and machine learning.
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 For Visual Understanding
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Author : Richa Singh
language : en
Publisher: Springer Nature
Release Date : 2020-01-08
Domain Adaptation For Visual Understanding written by Richa Singh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-08 with Computers categories.
This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field. The text presents a diverse selection of novel techniques, covering applications of object recognition, face recognition, and action and event recognition. Topics and features: reviews the domain adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning; proposes a unique way to determine which dataset is most useful in the base training, in order to improve the transferability of deep neural networks; describes a quantitative method for estimating the discrepancy between the source and target data to enhance image classification performance; presents a technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation; examines an original interpolation-based approach to address the issue of tracking model degradation in correlation filter-based methods. This authoritative work will serve as an invaluable reference for researchers and practitioners interested in machine learning-based visual recognition and understanding.
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
Computer Vision Eccv 2024
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Author : Aleš Leonardis
language : en
Publisher: Springer Nature
Release Date : 2024-10-31
Computer Vision Eccv 2024 written by Aleš Leonardis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-31 with Computers categories.
The multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024. The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation.
Domain Adaptation And Representation Transfer And Distributed And Collaborative Learning
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Author : Shadi Albarqouni
language : en
Publisher: Springer Nature
Release Date : 2020-09-25
Domain Adaptation And Representation Transfer And Distributed And Collaborative Learning written by Shadi Albarqouni and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-25 with Computers categories.
This book constitutes the refereed proceedings of the Second MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2020, and the First MICCAI Workshop on Distributed and Collaborative Learning, DCL 2020, held in conjunction with MICCAI 2020 in October 2020. The conference was planned to take place in Lima, Peru, but changed to an online format due to the Coronavirus pandemic. For DART 2020, 12 full papers were accepted from 18 submissions. They deal with methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical settings by making them robust and consistent across different domains. For DCL 2020, the 8 papers included in this book were accepted from a total of 12 submissions. They focus on the comparison, evaluation and discussion of methodological advancement and practical ideas about machine learning applied to problems where data cannot be stored in centralized databases; where information privacy is a priority; where it is necessary to deliver strong guarantees on the amount and nature of private information that may be revealed by the model as a result of training; and where it's necessary to orchestrate, manage and direct clusters of nodes participating in the same learning task.
Development And Analysis Of Deep Learning Architectures
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Author : Witold Pedrycz
language : en
Publisher: Springer Nature
Release Date : 2019-11-01
Development And Analysis Of Deep Learning Architectures written by Witold Pedrycz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-01 with Technology & Engineering categories.
This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of big data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical understanding of design. The book also discusses systematic design procedures, optimization techniques, and validation processes.
Computer Vision And Machine Intelligence
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Author : Massimo Tistarelli
language : en
Publisher: Springer Nature
Release Date : 2023-05-05
Computer Vision And Machine Intelligence written by Massimo Tistarelli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-05 with Technology & Engineering categories.
This book presents selected research papers on current developments in the fields of computer vision and machine intelligence from International Conference on Computer Vision and Machine Intelligence (CVMI 2022). The book covers topics in image processing, artificial intelligence, machine learning, deep learning, computer vision, machine intelligence, etc. The book is useful for researchers, postgraduate and undergraduate students, and professionals working in this domain.