Download or read book Domain Adaptation for Semantic and 3D Tasks written by Adrian Lopez Rodriguez and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Visual Domain Adaptation in the Deep Learning Era written by Gabriela Csurka and published by Morgan & Claypool Publishers. This book was released on 2022-04-05 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Person Re Identification written by Shaogang Gong and published by Springer Science & Business Media. This book was released on 2014-01-03 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Domain Adaptation in Computer Vision Applications written by Gabriela Csurka and published by Springer. This book was released on 2017-09-10 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Computer Vision ECCV 2022 written by Shai Avidan and published by Springer Nature. This book was released on 2022-10-29 with total page 807 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 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; object recognition; motion estimation.
Download or read book Pattern Recognition written by Christian Wallraven and published by Springer Nature. This book was released on 2022-05-10 with total page 599 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNCS 13188 - 13189 constitutes the refereed proceedings of the 6th Asian Conference on Pattern Recognition, ACPR 2021, held in Jeju Island, South Korea, in November 2021. The 85 full papers presented were carefully reviewed and selected from 154 submissions. The papers are organized in topics on: classification, action and video and motion, object detection and anomaly, segmentation, grouping and shape, face and body and biometrics, adversarial learning and networks, computational photography, learning theory and optimization, applications, medical and robotics, computer vision and robot vision.
Download or read book Domain Adaptation on Semantic Segmentation with Separate Affine Transformation in Batch Normalization written by Junhao Yan and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Domain adaptation on semantic segmentation generally refers to the procedures for narrowing the distribution gap between source and target data, which is vital for developing the automatic vehicle system. It requires a large amount of data with well-labelled ground truth at the pixel level. Labelling this scale of data is extremely costly due to the lot of human effort required. Also, manually labelling often comes with label noises that are harmful to automatic vehicle system development. In this case, solving the above problem utilizes computer-generated data and ground truth for development. However, a notorious problem exists when a system is trained with synthetic data but deployed in a real-world environment, which results from the distribution (domain) difference between these two kinds of data, and domain adaptation helps solve this issue. In the thesis, the limitation of conventional batch normalization layer on adversarial learning based domain adaptation methods is mentioned and discussed. From the view of the limitation, we propose replacing the Sharing Affine Transformation with our proposed Separate Affine Transformation (SEAT) to improve the domain adapting performance. The proposed SEAT is simple, easily implemented, and integrated into existing adversarial learning-based unsupervised domain adaptation methods. Also, to further improve the adaptation quality on lower-level features, we introduce multi-level adaptation by adding the lower-level features to the higher-level ones before feeding them to the discriminator, which is different from others by adding extra discriminators. Finally, a simple training strategy, self-training, is adopted to improve the model performance further. Extensive experiments show that our proposed method is able to get comparable results with other domain adaptation methods with simpler design.
Download or read book Domain Adaptation in Computer Vision with Deep Learning written by Hemanth Venkateswara and published by Springer Nature. This book was released on 2020-08-18 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2019-08-22 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11â€"12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop.
Download or read book Pattern Recognition and Computer Vision written by Qingshan Liu and published by Springer Nature. This book was released on 2023-12-23 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis.
Download or read book Autonomous Driving Perception written by Rui Fan and published by Springer Nature. This book was released on 2023-10-06 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the captivating world of computer vision and deep learning for autonomous driving with our comprehensive and in-depth guide. Immerse yourself in an in-depth exploration of cutting-edge topics, carefully crafted to engage tertiary students and ignite the curiosity of researchers and professionals in the field. From fundamental principles to practical applications, this comprehensive guide offers a gentle introduction, expert evaluations of state-of-the-art methods, and inspiring research directions. With a broad range of topics covered, it is also an invaluable resource for university programs offering computer vision and deep learning courses. This book provides clear and simplified algorithm descriptions, making it easy for beginners to understand the complex concepts. We also include carefully selected problems and examples to help reinforce your learning. Don't miss out on this essential guide to computer vision and deep learning for autonomous driving.
Download or read book Transfer Learning written by Qiang Yang and published by Cambridge University Press. This book was released on 2020-02-13 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.
Download or read book Computer Vision ECCV 2020 written by Andrea Vedaldi and published by Springer Nature. This book was released on 2020-11-12 with total page 830 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 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; object recognition; motion estimation.
Download or read book Pattern Recognition and Computer Vision written by Shiqi Yu and published by Springer Nature. This book was released on 2022-10-27 with total page 789 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 4-volume set LNCS 13534, 13535, 13536 and 13537 constitutes the refereed proceedings of the 5th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2022, held in Shenzhen, China, in November 2022. The 233 full papers presented were carefully reviewed and selected from 564 submissions. The papers have been organized in the following topical sections: Theories and Feature Extraction; Machine learning, Multimedia and Multimodal; Optimization and Neural Network and Deep Learning; Biomedical Image Processing and Analysis; Pattern Classification and Clustering; 3D Computer Vision and Reconstruction, Robots and Autonomous Driving; Recognition, Remote Sensing; Vision Analysis and Understanding; Image Processing and Low-level Vision; Object Detection, Segmentation and Tracking.
Download or read book Pattern Recognition ICPR International Workshops and Challenges written by Alberto Del Bimbo and published by Springer Nature. This book was released on 2021-02-20 with total page 767 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic. The 416 full papers presented in these 8 volumes were carefully reviewed and selected from about 700 submissions. The 46 workshops cover a wide range of areas including machine learning, pattern analysis, healthcare, human behavior, environment, surveillance, forensics and biometrics, robotics and egovision, cultural heritage and document analysis, retrieval, and women at ICPR2020.
Download or read book Handbook of Medical Image Computing and Computer Assisted Intervention written by S. Kevin Zhou and published by Academic Press. This book was released on 2019-10-18 with total page 1074 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention. Presents the key research challenges in medical image computing and computer-assisted intervention Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society Contains state-of-the-art technical approaches to key challenges Demonstrates proven algorithms for a whole range of essential medical imaging applications Includes source codes for use in a plug-and-play manner Embraces future directions in the fields of medical image computing and computer-assisted intervention
Download or read book Computer Vision ECCV 2018 Workshops written by Laura Leal-Taixé and published by Springer. This book was released on 2019-01-22 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six-volume set comprising the LNCS volumes 11129-11134 constitutes the refereed proceedings of the workshops that took place in conjunction with the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.43 workshops from 74 workshops proposals were selected for inclusion in the proceedings. The workshop topics present a good orchestration of new trends and traditional issues, built bridges into neighboring fields, and discuss fundamental technologies and novel applications.
