Domain Adaptation In Computer Vision Applications

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Domain Adaptation in Computer Vision with Deep Learning

Hemanth Venkateswara,Sethuraman Panchanathan
Domain Adaptation in Computer Vision with Deep Learning Book PDF

Author : Hemanth Venkateswara,Sethuraman Panchanathan
Publisher : Springer Nature
Page : 256 pages
File Size : 49,8 Mb
Release : 2020-08-18
Category : Computers
ISBN : 9783030455293

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Domain Adaptation in Computer Vision with Deep Learning by Hemanth Venkateswara,Sethuraman Panchanathan Pdf

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.

Domain Adaptation in Computer Vision Applications

Gabriela Csurka
Domain Adaptation in Computer Vision Applications Book PDF

Author : Gabriela Csurka
Publisher : Springer
Page : 344 pages
File Size : 45,5 Mb
Release : 2017-09-10
Category : Computers
ISBN : 9783319583471

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Domain Adaptation in Computer Vision Applications by Gabriela Csurka Pdf

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.

Visual Domain Adaptation in the Deep Learning Era

Gabriela Csurka,Timothy M. Hospedales,Mathieu Salzmann,Tatiana Tommasi
Visual Domain Adaptation in the Deep Learning Era Book PDF

Author : Gabriela Csurka,Timothy M. Hospedales,Mathieu Salzmann,Tatiana Tommasi
Publisher : Morgan & Claypool Publishers
Page : 190 pages
File Size : 55,7 Mb
Release : 2022-04-05
Category : Computers
ISBN : 9781636393421

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Visual Domain Adaptation in the Deep Learning Era by Gabriela Csurka,Timothy M. Hospedales,Mathieu Salzmann,Tatiana Tommasi Pdf

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 for Visual Understanding

Richa Singh,Mayank Vatsa,Vishal M. Patel,Nalini Ratha
Domain Adaptation for Visual Understanding Book PDF

Author : Richa Singh,Mayank Vatsa,Vishal M. Patel,Nalini Ratha
Publisher : Springer Nature
Page : 144 pages
File Size : 43,6 Mb
Release : 2020-01-08
Category : Computers
ISBN : 9783030306717

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Domain Adaptation for Visual Understanding by Richa Singh,Mayank Vatsa,Vishal M. Patel,Nalini Ratha Pdf

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.

Domain Adaptation for Visual Recognition

Raghuraman Gopalan,Vishal M. Patel,Ruonan Li,Rama Chellappa
Domain Adaptation for Visual Recognition Book PDF

Author : Raghuraman Gopalan,Vishal M. Patel,Ruonan Li,Rama Chellappa
Publisher : Unknown
Page : 93 pages
File Size : 43,8 Mb
Release : 2015
Category : Computer vision
ISBN : 1680830317

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Domain Adaptation for Visual Recognition by Raghuraman Gopalan,Vishal M. Patel,Ruonan Li,Rama Chellappa Pdf

Domain adaptation is an active, emerging research area that attempts to address the changes in data distribution across training and testing datasets. With the availability of a multitude of image acquisition sensors, variations due to illumination, and viewpoint among others, computer vision applications present a very natural test bed for evaluating domain adaptation methods. In this monograph, we provide a comprehensive overview of domain adaptation solutions for visual recognition problems. By starting with the problem description and illustrations, we discuss three adaptation scenarios namely, (i) unsupervised adaptation where the "source domain" training data is partially labeled and the "target domain" test data is unlabeled, (ii) semi-supervised adaptation where the target domain also has partial labels, and (iii) multi-domain heterogeneous adaptation which studies the previous two settings with the source and/or target having more than one domain, and accounts for cases where the features used to represent the data in each domain are different. For all these topics we discuss existing adaptation techniques in the literature, which are motivated by the principles of max-margin discriminative learning, manifold learning, sparse coding, as well as low-rank representations. These techniques have shown improved performance on a variety of applications such as object recognition, face recognition, activity analysis, concept classification, and person detection. We then conclude by analyzing the challenges posed by the realm of "big visual data", in terms of the generalization ability of adaptation algorithms to unconstrained data acquisition as well as issues related to their computational tractability, and draw parallels with the efforts from vision community on image transformation models, and invariant descriptors so as to facilitate improved understanding of vision problems under uncertainty.

Advances and Applications in Deep Learning

Anonim
Advances and Applications in Deep Learning Book PDF

Author : Anonim
Publisher : BoD – Books on Demand
Page : 124 pages
File Size : 49,8 Mb
Release : 2020-12-09
Category : Computers
ISBN : 9781839628788

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Advances and Applications in Deep Learning by Anonim Pdf

Artificial Intelligence (AI) has attracted the attention of researchers and users alike and is taking an increasingly crucial role in our modern society. From cars, smartphones, and airplanes to medical equipment, consumer applications, and industrial machines, the impact of AI is notoriously changing the world we live in. In this context, Deep Learning (DL) is one of the techniques that has taken the lead for cognitive processes, pattern recognition, object detection, and machine learning, all of which have played a crucial role in the growth of AI. As such, this book examines DL applications and future trends in the field. It is a useful resource for researchers and students alike.

Visual Object Recognition

Kristen Thielscher,Bastian Chernova
Visual Object Recognition Book PDF

Author : Kristen Thielscher,Bastian Chernova
Publisher : Springer Nature
Page : 163 pages
File Size : 51,7 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783031015533

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Visual Object Recognition by Kristen Thielscher,Bastian Chernova Pdf

The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. Table of Contents: Introduction / Overview: Recognition of Specific Objects / Local Features: Detection and Description / Matching Local Features / Geometric Verification of Matched Features / Example Systems: Specific-Object Recognition / Overview: Recognition of Generic Object Categories / Representations for Object Categories / Generic Object Detection: Finding and Scoring Candidates / Learning Generic Object Category Models / Example Systems: Generic Object Recognition / Other Considerations and Current Challenges / Conclusions

Domain Adaptation and Representation Transfer

Lisa Koch,M. Jorge Cardoso,Enzo Ferrante,Konstantinos Kamnitsas,Mobarakol Islam,Meirui Jiang,Nicola Rieke,Sotirios A. Tsaftaris,Dong Yang
Domain Adaptation and Representation Transfer Book PDF

Author : Lisa Koch,M. Jorge Cardoso,Enzo Ferrante,Konstantinos Kamnitsas,Mobarakol Islam,Meirui Jiang,Nicola Rieke,Sotirios A. Tsaftaris,Dong Yang
Publisher : Springer Nature
Page : 180 pages
File Size : 46,5 Mb
Release : 2023-10-13
Category : Computers
ISBN : 9783031458576

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Domain Adaptation and Representation Transfer by Lisa Koch,M. Jorge Cardoso,Enzo Ferrante,Konstantinos Kamnitsas,Mobarakol Islam,Meirui Jiang,Nicola Rieke,Sotirios A. Tsaftaris,Dong Yang Pdf

This book constitutes the refereed proceedings of the 5th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2023, which was held in conjunction with MICCAI 2023, in October 2023. The 16 full papers presented in this book were carefully reviewed and selected from 32 submissions. They discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains.

Computer Vision -- ECCV 2010

Kostas Daniilidis,Petros Maragos,Nikos Paragios
Computer Vision ECCV 2010 Book PDF

Author : Kostas Daniilidis,Petros Maragos,Nikos Paragios
Publisher : Springer Science & Business Media
Page : 836 pages
File Size : 54,6 Mb
Release : 2010-08-30
Category : Computers
ISBN : 9783642155604

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Computer Vision -- ECCV 2010 by Kostas Daniilidis,Petros Maragos,Nikos Paragios Pdf

The six-volume set comprising LNCS volumes 6311 until 6313 constitutes the refereed proceedings of the 11th European Conference on Computer Vision, ECCV 2010, held in Heraklion, Crete, Greece, in September 2010. The 325 revised papers presented were carefully reviewed and selected from 1174 submissions. The papers are organized in topical sections on object and scene recognition; segmentation and grouping; face, gesture, biometrics; motion and tracking; statistical models and visual learning; matching, registration, alignment; computational imaging; multi-view geometry; image features; video and event characterization; shape representation and recognition; stereo; reflectance, illumination, color; medical image analysis.

Computer Vision, Pattern Recognition, Image Processing, and Graphics

R. Venkatesh Babu,Mahadeva Prasanna,Vinay P. Namboodiri
Computer Vision Pattern Recognition Image Processing and Graphics Book PDF

Author : R. Venkatesh Babu,Mahadeva Prasanna,Vinay P. Namboodiri
Publisher : Springer Nature
Page : 642 pages
File Size : 54,8 Mb
Release : 2020-11-16
Category : Computers
ISBN : 9789811586972

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Computer Vision, Pattern Recognition, Image Processing, and Graphics by R. Venkatesh Babu,Mahadeva Prasanna,Vinay P. Namboodiri Pdf

This book constitutes the refereed proceedings of the 7th National Conference on Computer Vision, Pattern Recognition, Image Processing, and Graphics, NCVPRIPG 2019, held in Hubballi, India, in December 2019. The 55 revised full papers 3 short papers presented in this volume were carefully reviewed and selected from 210 submissions. The papers are organized in topical sections on vision and geometry, learning and vision, image processing and document analysis, detection and recognition.

Visual Domain Adaptation in the Deep Learning Era

Gabriela Csurka,Timothy M. Hospedales,Mathieu Salzmann,Tatiana Tommasi
Visual Domain Adaptation in the Deep Learning Era Book PDF

Author : Gabriela Csurka,Timothy M. Hospedales,Mathieu Salzmann,Tatiana Tommasi
Publisher : Springer Nature
Page : 182 pages
File Size : 52,8 Mb
Release : 2022-06-06
Category : Computers
ISBN : 9783031791758

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Visual Domain Adaptation in the Deep Learning Era by Gabriela Csurka,Timothy M. Hospedales,Mathieu Salzmann,Tatiana Tommasi Pdf

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.

Computer Vision – ACCV 2018

C. V. Jawahar,Hongdong Li,Greg Mori,Konrad Schindler
Computer Vision ACCV 2018 Book PDF

Author : C. V. Jawahar,Hongdong Li,Greg Mori,Konrad Schindler
Publisher : Springer
Page : 736 pages
File Size : 53,7 Mb
Release : 2019-05-28
Category : Computers
ISBN : 9783030208936

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Computer Vision – ACCV 2018 by C. V. Jawahar,Hongdong Li,Greg Mori,Konrad Schindler Pdf

The six volume set LNCS 11361-11366 constitutes the proceedings of the 14th Asian Conference on Computer Vision, ACCV 2018, held in Perth, Australia, in December 2018. The total of 274 contributions was carefully reviewed and selected from 979 submissions during two rounds of reviewing and improvement. The papers focus on motion and tracking, segmentation and grouping, image-based modeling, dep learning, object recognition object recognition, object detection and categorization, vision and language, video analysis and event recognition, face and gesture analysis, statistical methods and learning, performance evaluation, medical image analysis, document analysis, optimization methods, RGBD and depth camera processing, robotic vision, applications of computer vision.

Person Re-Identification

Shaogang Gong,Marco Cristani,Shuicheng Yan,Chen Change Loy
Person Re Identification Book PDF

Author : Shaogang Gong,Marco Cristani,Shuicheng Yan,Chen Change Loy
Publisher : Springer Science & Business Media
Page : 445 pages
File Size : 50,8 Mb
Release : 2014-01-03
Category : Computers
ISBN : 9781447162964

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Person Re-Identification by Shaogang Gong,Marco Cristani,Shuicheng Yan,Chen Change Loy Pdf

The first book of its kind dedicated to the challenge of person re-identification, this text provides an in-depth, multidisciplinary discussion of recent developments and state-of-the-art methods. Features: introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms and examines the benefits of semantic attributes; describes how to segregate meaningful body parts from background clutter; examines the use of 3D depth images and contextual constraints derived from the visual appearance of a group; reviews approaches to feature transfer function and distance metric learning and discusses potential solutions to issues of data scalability and identity inference; investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference and describes techniques for improving post-rank search efficiency; explores the design rationale and implementation considerations of building a practical re-identification system.

Computer Vision – ECCV 2018

Vittorio Ferrari,Martial Hebert,Cristian Sminchisescu,Yair Weiss
Computer Vision ECCV 2018 Book PDF

Author : Vittorio Ferrari,Martial Hebert,Cristian Sminchisescu,Yair Weiss
Publisher : Springer
Page : 855 pages
File Size : 54,7 Mb
Release : 2018-10-06
Category : Computers
ISBN : 9783030012373

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Computer Vision – ECCV 2018 by Vittorio Ferrari,Martial Hebert,Cristian Sminchisescu,Yair Weiss Pdf

The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.

Synthetic Data for Deep Learning

Sergey I. Nikolenko
Synthetic Data for Deep Learning Book PDF

Author : Sergey I. Nikolenko
Publisher : Springer Nature
Page : 348 pages
File Size : 51,8 Mb
Release : 2021-06-26
Category : Computers
ISBN : 9783030751784

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Synthetic Data for Deep Learning by Sergey I. Nikolenko Pdf

This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for years to come. The book can also serve as an introduction to several other important subfields of machine learning that are seldom touched upon in other books. Machine learning as a discipline would not be possible without the inner workings of optimization at hand. The book includes the necessary sinews of optimization though the crux of the discussion centers on the increasingly popular tool for training deep learning models, namely synthetic data. It is expected that the field of synthetic data will undergo exponential growth in the near future. This book serves as a comprehensive survey of the field. In the simplest case, synthetic data refers to computer-generated graphics used to train computer vision models. There are many more facets of synthetic data to consider. In the section on basic computer vision, the book discusses fundamental computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., object detection and semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, and simulation environments for robotics. Additionally, it touches upon applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more). It also surveys the work on improving synthetic data development and alternative ways to produce it such as GANs. The book introduces and reviews several different approaches to synthetic data in various domains of machine learning, most notably the following fields: domain adaptation for making synthetic data more realistic and/or adapting the models to be trained on synthetic data and differential privacy for generating synthetic data with privacy guarantees. This discussion is accompanied by an introduction into generative adversarial networks (GAN) and an introduction to differential privacy.