Domain Adaptation For Visual Recognition

Author : Raghuraman Gopalan,Vishal M. Patel,Ruonan Li,Rama Chellappa
Publisher : Unknown
Page : 93 pages
File Size : 51,8 Mb
Release : 2015
Category : Computer vision
ISBN : 1680830317

Get Book

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.

Author : Richa Singh,Mayank Vatsa,Vishal M. Patel,Nalini Ratha
Publisher : Springer Nature
Page : 144 pages
File Size : 41,7 Mb
Release : 2020-01-08
Category : Computers
ISBN : 9783030306717

Get Book

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.

Author : Gabriela Csurka,Timothy M. Hospedales,Mathieu Salzmann,Tatiana Tommasi
Publisher : Springer Nature
Page : 182 pages
File Size : 49,8 Mb
Release : 2022-06-06
Category : Computers
ISBN : 9783031791758

Get Book

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.

Author : Gabriela Csurka
Publisher : Springer
Page : 344 pages
File Size : 51,5 Mb
Release : 2017-09-10
Category : Computers
ISBN : 9783319583471

Get Book

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.

Author : Hemanth Venkateswara,Sethuraman Panchanathan
Publisher : Springer Nature
Page : 256 pages
File Size : 48,9 Mb
Release : 2020-08-18
Category : Computers
ISBN : 9783030455293

Get Book

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.

Author : Duc-Nghia Pham,Seong-Bae Park
Publisher : Springer
Page : 1102 pages
File Size : 53,9 Mb
Release : 2014-11-12
Category : Computers
ISBN : 9783319135601

Get Book

PRICAI 2014: Trends in Artificial Intelligence by Duc-Nghia Pham,Seong-Bae Park Pdf

This book constitutes the refereed proceedings of the 13th Pacific Rim Conference on Artificial Intelligence, PRICAI 2014, held in Gold Coast, Queensland, Australia, in December 2014. The 74 full papers and 20 short papers presented in this volume were carefully reviewed and selected from 203 submissions. The topics include inference; reasoning; robotics; social intelligence. AI foundations; applications of AI; agents; Bayesian networks; neural networks; Markov networks; bioinformatics; cognitive systems; constraint satisfaction; data mining and knowledge discovery; decision theory; evolutionary computation; games and interactive entertainment; heuristics; knowledge acquisition and ontology; knowledge representation, machine learning; multimodal interaction; natural language processing; planning and scheduling; probabilistic.

Author : Kostas Daniilidis,Petros Maragos,Nikos Paragios
Publisher : Springer Science & Business Media
Page : 836 pages
File Size : 46,6 Mb
Release : 2010-08-30
Category : Computers
ISBN : 9783642155604

Get Book

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.

Author : Kristen Thielscher,Bastian Chernova
Publisher : Springer Nature
Page : 163 pages
File Size : 43,9 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783031015533

Get Book

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

Author : Gabriela Csurka,Timothy M. Hospedales,Mathieu Salzmann,Tatiana Tommasi
Publisher : Morgan & Claypool Publishers
Page : 190 pages
File Size : 45,7 Mb
Release : 2022-04-05
Category : Computers
ISBN : 9781636393421

Get Book

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.

Author : Jingjing Li
Publisher : Springer Nature
Page : 234 pages
File Size : 48,6 Mb
Release : 2024-06-16
Category : Electronic
ISBN : 9789819710256

Get Book

Unsupervised Domain Adaptation by Jingjing Li Pdf

Author : Qian Wang,Fausto Milletari,Hien V. Nguyen,Shadi Albarqouni,M. Jorge Cardoso,Nicola Rieke,Ziyue Xu,Konstantinos Kamnitsas,Vishal Patel,Badri Roysam,Steve Jiang,Kevin Zhou,Khoa Luu,Ngan Le
Publisher : Springer Nature
Page : 254 pages
File Size : 42,8 Mb
Release : 2019-10-13
Category : Computers
ISBN : 9783030333911

Get Book

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data by Qian Wang,Fausto Milletari,Hien V. Nguyen,Shadi Albarqouni,M. Jorge Cardoso,Nicola Rieke,Ziyue Xu,Konstantinos Kamnitsas,Vishal Patel,Badri Roysam,Steve Jiang,Kevin Zhou,Khoa Luu,Ngan Le Pdf

This book constitutes the refereed proceedings of the First MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the First International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. DART 2019 accepted 12 papers for publication out of 18 submissions. The papers deal with methodological advancements and ideas that can improve the applicability of machine learning and deep learning approaches to clinical settings by making them robust and consistent across different domains. MIL3ID accepted 16 papers out of 43 submissions for publication, dealing with best practices in medical image learning with label scarcity and data imperfection.

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 : 51,6 Mb
Release : 2023-10-13
Category : Computers
ISBN : 9783031458576

Get Book

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.

Author : Gang Hua,Hervé Jégou
Publisher : Springer
Page : 919 pages
File Size : 54,8 Mb
Release : 2016-11-23
Category : Computers
ISBN : 9783319494098

Get Book

Computer Vision – ECCV 2016 Workshops by Gang Hua,Hervé Jégou Pdf

The three-volume set LNCS 9913, LNCS 9914, and LNCS 9915 comprises the refereed proceedings of the Workshops that took place in conjunction with the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The three-volume set LNCS 9913, LNCS 9914, and LNCS 9915 comprises the refereed proceedings of the Workshops that took place in conjunction with the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. 27 workshops from 44 workshops proposals were selected for inclusion in the proceedings. These address the following themes: Datasets and Performance Analysis in Early Vision; Visual Analysis of Sketches; Biological and Artificial Vision; Brave New Ideas for Motion Representations; Joint ImageNet and MS COCO Visual Recognition Challenge; Geometry Meets Deep Learning; Action and Anticipation for Visual Learning; Computer Vision for Road Scene Understanding and Autonomous Driving; Challenge on Automatic Personality Analysis; BioImage Computing; Benchmarking Multi-Target Tracking: MOTChallenge; Assistive Computer Vision and Robotics; Transferring and Adapting Source Knowledge in Computer Vision; Recovering 6D Object Pose; Robust Reading; 3D Face Alignment in the Wild and Challenge; Egocentric Perception, Interaction and Computing; Local Features: State of the Art, Open Problems and Performance Evaluation; Crowd Understanding; Video Segmentation; The Visual Object Tracking Challenge Workshop; Web-scale Vision and Social Media; Computer Vision for Audio-visual Media; Computer VISion for ART Analysis; Virtual/Augmented Reality for Visual Artificial Intelligence; Joint Workshop on Storytelling with Images and Videos and Large Scale Movie Description and Understanding Challenge.

Author : Shadi Albarqouni,Spyridon Bakas,Konstantinos Kamnitsas,M. Jorge Cardoso,Bennett Landman,Wenqi Li,Fausto Milletari,Nicola Rieke,Holger Roth,Daguang Xu,Ziyue Xu
Publisher : Springer Nature
Page : 224 pages
File Size : 43,5 Mb
Release : 2020-09-25
Category : Computers
ISBN : 9783030605483

Get Book

Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning by Shadi Albarqouni,Spyridon Bakas,Konstantinos Kamnitsas,M. Jorge Cardoso,Bennett Landman,Wenqi Li,Fausto Milletari,Nicola Rieke,Holger Roth,Daguang Xu,Ziyue Xu Pdf

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.

Author : Yun Fu
Publisher : Springer
Page : 236 pages
File Size : 44,9 Mb
Release : 2014-10-30
Category : Computers
ISBN : 9783319120003

Get Book

Low-Rank and Sparse Modeling for Visual Analysis by Yun Fu Pdf

This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications.