Unsupervised Domain Adaptation for Semantic Segmentation of NIR Images through Generative Latent Search

TL;DR

This paper introduces a novel unsupervised domain adaptation method for skin segmentation in near-infrared images, leveraging generative models to find target image proxies without target domain labels, improving segmentation accuracy.

Contribution

The paper proposes a target-independent segmentation approach using latent space optimization to find image proxies, advancing unsupervised domain adaptation techniques for NIR skin segmentation.

Findings

Outperforms state-of-the-art UDA methods on NIR skin datasets

Achieves state-of-the-art results in Synthia to Cityscapes adaptation

Demonstrates effectiveness without access to target domain data

Abstract

Segmentation of the pixels corresponding to human skin is an essential first step in multiple applications ranging from surveillance to heart-rate estimation from remote-photoplethysmography. However, the existing literature considers the problem only in the visible-range of the EM-spectrum which limits their utility in low or no light settings where the criticality of the application is higher. To alleviate this problem, we consider the problem of skin segmentation from the Near-infrared images. However, Deep learning based state-of-the-art segmentation techniques demands large amounts of labelled data that is unavailable for the current problem. Therefore we cast the skin segmentation problem as that of target-independent Unsupervised Domain Adaptation (UDA) where we use the data from the Red-channel of the visible-range to develop skin segmentation algorithm on NIR images. We propose a method for target-independent segmentation where the 'nearest-clone' of a target image in the source domain is searched and used as a proxy in the segmentation network trained only on the source domain. We prove the existence of 'nearest-clone' and propose a method to find it through an optimization algorithm over the latent space of a Deep generative model based on variational inference. We demonstrate the efficacy of the proposed method for NIR skin segmentation over the state-of-the-art UDA segmentation methods on the two newly created skin segmentation datasets in NIR domain despite not having access to the target NIR data. Additionally, we report state-of-the-art results for adaption from Synthia to Cityscapes which is a popular setting in Unsupervised Domain Adaptation for semantic segmentation. The code and datasets are available at https://github.com/ambekarsameer96/GLSS.