Domain Adaptation for Visual Applications: A Comprehensive Survey
This is an incremental survey paper that organizes existing knowledge about domain adaptation for computer vision researchers.
This paper provides a comprehensive survey of domain adaptation methods specifically for visual applications, covering historical shallow methods, deep learning approaches, and extensions beyond image categorization.
The aim of this paper is to give an overview of domain adaptation and transfer learning with a specific view on visual applications. After a general motivation, we first position domain adaptation in the larger transfer learning problem. Second, we try to address and analyze briefly the state-of-the-art methods for different types of scenarios, first describing the historical shallow methods, addressing both the homogeneous and the heterogeneous domain adaptation methods. Third, we discuss the effect of the success of deep convolutional architectures which led to new type of domain adaptation methods that integrate the adaptation within the deep architecture. Fourth, we overview the methods that go beyond image categorization, such as object detection or image segmentation, video analyses or learning visual attributes. Finally, we conclude the paper with a section where we relate domain adaptation to other machine learning solutions.