Domain Adaptation for Visual Understanding

  • Richa Singh
  • Mayank Vatsa
  • Vishal M. Patel
  • Nalini Ratha

Table of contents

  1. Front Matter
    Pages i-x
  2. Soumyadeep Ghosh, Richa Singh, Mayank Vatsa, Nalini Ratha, Vishal M. Patel
    Pages 1-15
  3. Issam H. Laradji, Reza Babanezhad
    Pages 17-31
  4. Amélie Royer, Konstantinos Bousmalis, Stephan Gouws, Fred Bertsch, Inbar Mosseri, Forrester Cole et al.
    Pages 33-49
  5. Parijat Dube, Bishwaranjan Bhattacharjee, Elisabeth Petit-Bois, Matthew Hill
    Pages 51-64
  6. Xinyan Yu, Ya Zhang, Rui Zhang
    Pages 65-79
  7. Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh, Sridha Sridharan
    Pages 81-94
  8. Nagashri N. Lakshminarayana, Deen Dayal Mohan, Nishant Sankaran, Srirangaraj Setlur, Venu Govindaraju
    Pages 95-109
  9. Anush Sankaran, Mayank Vatsa, Richa Singh
    Pages 111-127
  10. Xiyu Kong, Qiping Zhou, Yunyu Lai, Muming Zhao, Chongyang Zhang
    Pages 129-142
  11. Back Matter
    Pages 143-144

About this book


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.

Dr. Richa Singh is a Professor at Indraprastha Institute of Information Technology, Delhi, India. Dr. Mayank Vatsa is a Professor at the same institution. Dr. Vishal M. Patel is an Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, Baltimore, MD, USA. Dr. Nalini Ratha is a Research Staff Member at the IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA.


Domain Adaptation Machine Learning Computer Vision Representation Learning Transfer Learning Generative Adversarial Network Metric Learning Reinforcement Learning

Editors and affiliations

  1. 1.Indraprastha Institute of Information Technology DelhiNew DelhiIndia
  2. 2.Indraprastha Institute of Information Technology DelhiNew DelhiIndia
  3. 3.Johns Hopkins UniversityBaltimoreUSA
  4. 4.IBM Thomas J. Watson Research CenterYorktown HeightsUSA

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