Work

Cross-domain autonomous driving visual segmentation based on enhanced target data learning

Year: 2024

Type: article

Abstract: Within the broader context of Information and Communications Technology (ICT), the quest for reliable and scalable visual segmentation methods poses significant challenges, particularly in autonomous driving, where real-world scene complexity requires advanced solutions. To address data scarcity and improve segmentation performance, we propose a novel unsupervised domain adaptation (UDA) approach that enhances target domain learning. Our method introduces multiple perturbations consistency, leveraging spatial context within the target domain to improve recognition. By applying perturbations at input and feature levels and using a consistency loss, we enhance contextual learning. Additionally, a weight mapping technique reduces the impact of detrimental source domain information. Experimental results demonstrate that our approach outperforms baseline methods on the GTAV → Cityscapes and SYNTHIA → Cityscapes datasets.

Source: ICT Express

DOI: 10.1016/j.icte.2024.09.020

Authors: Chaoyu Rao, Xiaoyong Fang, Yunzhe Zhang, Wanshu Fan, Dongsheng Zhou

Institutions: Dalian University, Hunan Institute of Technology, Dalian University of Technology

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,500