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Domain Adaptation in Computer Vision Applications

  • Book
  • © 2017

Overview

Editors:
  1. Gabriela Csurka

    1. Naver Labs Europe, Meylan, France

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  • The first book focused on domain adaptation for visual applications
  • Provides a comprehensive experimental study, highlighting the strengths and weaknesses of popular methods, and introducing new and more challenging datasets
  • Presents an historical overview of research in this area
  • Covers such tasks as object detection, image segmentation and video application, where the need for domain adaptation has been rarely addressed by the community
  • Considers real-world, industrial applications, and solutions for cases where existing methods might not be applicable
  • Includes supplementary material: sn.pub/extras
  • Includes supplementary material: sn.pub/extras

Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)

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Table of contents (16 chapters)

  1. Front Matter

    Pages i-x
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  2. A Comprehensive Survey on Domain Adaptation for Visual Applications

    • Gabriela Csurka
    Pages 1-35

  3. A Deeper Look at Dataset Bias

    • Tatiana Tommasi, Novi Patricia, Barbara Caputo, Tinne Tuytelaars
    Pages 37-55

  4. Shallow Domain Adaptation Methods

    1. Front Matter

      Pages 57-57
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    2. Geodesic Flow Kernel and Landmarks: Kernel Methods for Unsupervised Domain Adaptation

      • Boqing Gong, Kristen Grauman, Fei Sha
      Pages 59-79

    3. Unsupervised Domain Adaptation Based on Subspace Alignment

      • Basura Fernando, Rahaf Aljundi, Rémi Emonet, Amaury Habrard, Marc Sebban, Tinne Tuytelaars
      Pages 81-94

    4. Learning Domain Invariant Embeddings by Matching Distributions

      • Mahsa Baktashmotlagh, Mehrtash Harandi, Mathieu Salzmann
      Pages 95-114

    5. Adaptive Transductive Transfer Machines: A Pipeline for Unsupervised Domain Adaptation

      • Nazli Farajidavar, Teofilo de Campos, Josef Kittler
      Pages 115-132

    6. What to Do When the Access to the Source Data Is Constrained?

      • Gabriela Csurka, Boris Chidlovskii, Stéphane Clinchant
      Pages 133-149

  5. Deep Domain Adaptation Methods

    1. Front Matter

      Pages 151-151
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    2. Correlation Alignment for Unsupervised Domain Adaptation

      • Baochen Sun, Jiashi Feng, Kate Saenko
      Pages 153-171

    3. Simultaneous Deep Transfer Across Domains and Tasks

      • Judy Hoffman, Eric Tzeng, Trevor Darrell, Kate Saenko
      Pages 173-187

    4. Domain-Adversarial Training of Neural Networks

      • Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette et al.
      Pages 189-209

  6. Beyond Image Classification

    1. Front Matter

      Pages 211-211
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    2. Unsupervised Fisher Vector Adaptation for Re-identification

      • Usman Tariq, Jose A. Rodriguez-Serrano, Florent Perronnin
      Pages 213-225

    3. Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA

      • German Ros, Laura Sellart, Gabriel Villalonga, Elias Maidanik, Francisco Molero, Marc Garcia et al.
      Pages 227-241

    4. From Virtual to Real World Visual Perception Using Domain Adaptation—The DPM as Example

      • Antonio M. López, Jiaolong Xu, José L. Gómez, David Vázquez, Germán Ros
      Pages 243-258

    5. Generalizing Semantic Part Detectors Across Domains

      • David Novotny, Diane Larlus, Andrea Vedaldi
      Pages 259-273

  7. Beyond Domain Adaptation: Unifying Perspectives

    1. Front Matter

      Pages 275-275
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    2. A Multisource Domain Generalization Approach to Visual Attribute Detection

      • Chuang Gan, Tianbao Yang, Boqing Gong
      Pages 277-289
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Keywords

About this book

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.

Editors and Affiliations

  • Naver Labs Europe, Meylan, France

    Gabriela Csurka

About the editor

Dr. Gabriela Csurka is a Senior Scientist in the Computer Vision Team at Naver Labs Europe, Meylan, France.

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