Domain Adaptation in Computer Vision Applications / edited by Gabriela Csurka.

Format:

Book

Contributor:

Csurka, Gabriela, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Advances in computer vision and pattern recognition 2191-6586

Advances in Computer Vision and Pattern Recognition, 2191-6586

Language:

English

Subjects (All):

Optical data processing.

Artificial intelligence.

Application software.

Image Processing and Computer Vision.

Artificial Intelligence.

Computer Appl. in Administrative Data Processing.

Local Subjects:

Image Processing and Computer Vision.

Artificial Intelligence.

Computer Appl. in Administrative Data Processing.

Physical Description:

1 online resource (X, 344 pages) : 107 illustrations, 101 illustrations in color.

Edition:

First edition 2017.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2017.

System Details:

text file PDF

Summary:

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.

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.

Other Format:

Printed edition:

ISBN:

978-3-319-58347-1

9783319583471

Access Restriction:

Restricted for use by site license.