Domain Adaptation in Computer Vision Applications

1. Front Matter

   Pages i-x

   PDF

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

      PDF

   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

      PDF

   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

      PDF

   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

      PDF

   2. A Multisource Domain Generalization Approach to Visual Attribute Detection

      • Chuang Gan, Tianbao Yang, Boqing Gong

      Pages 277-289

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.

• Computer Vision
• Visual Applications
• Image Categorization
• Pattern Recognition
• Data Analytics
• Unsupervised Domain Adaptation
• Transductive Transfer Learning
• Domain Shift
• Feature Transformation
• Subspace Learning
• Landmark Selection
• Maximum Mean Discrepancy
• Grassman Manifold
• Geodesic Flow
• Subspace Alignment
• Marginalized Denoising Autoencoders
• Deep Learning
• Domain-Adversarial Training

• Naver Labs Europe, Meylan, France

  Gabriela Csurka

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

Policies and ethics