rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Domain Adaptation in Computer Vision Applications
  name:
    name part: Csurka, Gabriela.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: X, 344 p. 107 illus., 101 illus. in color. online resource.
  abstract: 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. Dr. Gabriela Csurka is a Senior Scientist in the Computer Vision Team at Xerox Research Centre Europe, Meylan, France.
  table of contents: A Comprehensive Survey on Domain Adaptation for Visual Applications -- A Deeper Look at Dataset Bias.- Part I: Shallow Domain Adaptation Methods -- Geodesic Flow Kernel and Landmarks: Kernel Methods for Unsupervised Domain Adaptation -- Unsupervised Domain Adaptation based on Subspace Alignment -- Learning Domain Invariant Embeddings by Matching Distributions -- Adaptive Transductive Transfer Machines: A Pipeline for Unsupervised Domain Adaptation -- What To Do When the Access to the Source Data is Constrained?.- Part II: Deep Domain Adaptation Methods -- Correlation Alignment for Unsupervised Domain Adaptation -- Simultaneous Deep Transfer Across Domains and Tasks -- Domain-Adversarial Training of Neural Networks.- Part III: Beyond Image Classification -- Unsupervised Fisher Vector Adaptation for Re-Identification -- Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA -- From Virtual to Real World Visual Perception using Domain Adaptation – The DPM as Example -- Generalizing Semantic Part Detectors Across Domains.- Part IV: Beyond Domain Adaptation: Unifying Perspectives -- A Multi-Source Domain Generalization Approach to Visual Attribute Detection -- Unifying Multi-Domain Multi-Task Learning: Tensor and Neural Network Perspectives.
  note: edited by Gabriela Csurka.
  subject:
    topic: Computer science
  subject:
    topic: Artificial intelligence
  subject:
    topic: Image processing
  subject:
    topic: Application software
  subject:
    topic: Computer Science
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Computer Appl. in Administrative Data Processing
  classification: TA1637-1638
  classification: TA1634
  classification: 006.6
  classification: 006.37
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Advances in Computer Vision and Pattern Recognition
  identifier: 9783319583471
  identifier: http://dx.doi.org/10.1007/978-3-319-58347-1
  record info:
    record creation date: 170912
    record change date: 20170919021223.0
    record identifier: 978-3-319-58347-1