Unsupervised Domain Adaptation in Semantic Segmentation: a Review

Abstract

The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation. This task is attracting a wide interest, since semantic segmentation models require a huge amount of labeled data and the lack of data fitting specific requirements is the main limitation in the deployment of these techniques. This problem has been recently explored and has rapidly grown with a large number of ad-hoc approaches. This motivates us to build a comprehensive overview of the proposed methodologies and to provide a clear categorization. In this paper, we start by introducing the problem, its formulation and the various scenarios that can be considered. Then, we introduce the different levels at which adaptation strategies may be applied: namely, at the input (image) level, at the internal features representation and at the output level. Furthermore, we present a detailed overview of the literature in the field, dividing previous methods based on the following (non mutually exclusive) categories: adversarial learning, generative-based, analysis of the classifier discrepancies, self-teaching, entropy minimization, curriculum learning and multi-task learning. Novel research directions are also briefly introduced to give a hint of interesting open problems in the field. Finally, a comparison of the performance of the various methods in the widely used autonomous driving scenario is presented.

34 pages, 7 figures, 2 tables

References in corpus (19)

• Overcoming catastrophic forgetting in neural networks
• Rethinking Atrous Convolution for Semantic Image Segmentation
• Deep Visual Domain Adaptation: A Survey
• ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
• FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
• STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation
• Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes
• Domain Adaptation for Visual Applications: A Comprehensive Survey
• CARLA: An Open Urban Driving Simulator
• Fully Convolutional Multi-Class Multiple Instance Learning
• Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
• Universal Domain Adaptation through Self Supervision
• Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation
• Domain Stylization: A Strong, Simple Baseline for Synthetic to Real Image Domain Adaptation
• FDA: Fourier Domain Adaptation for Semantic Segmentation
• Semi and Weakly Supervised Semantic Segmentation Using Generative Adversarial Network
• Unsupervised Domain Adaptation for Mobile Semantic Segmentation based on Cycle Consistency and Feature Alignment
• Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach
• Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation

Cited by in corpus (10)

• A Survey on Deep Domain Adaptation for LiDAR Perception
• Prototype Guided Federated Learning of Visual Feature Representations
• Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer
• Finding an Unsupervised Image Segmenter in Each of Your Deep Generative Models
• Conterfactual Generative Zero-Shot Semantic Segmentation
• Practical Evaluation of Out-of-Distribution Detection Methods for Image Classification
• ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation
• Unsupervised Domain Adaptation Network with Category-Centric Prototype Aligner for Biomedical Image Segmentation
• Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models
• Unsupervised Domain Adaptation: A Reality Check