Cross-domain few-shot semantic segmentation for the astronaut work environment

Abstract

The study of few-shot semantic segmentation (FSS) for the astronaut work environment (AWE) is of significant importance as it enables the segmentation of unknown categories. However, general FSS methods are predicated on the assumption that the training and testing data belong to the same domain. When this assumption is invalid, the model’s performance is significantly degraded. We propose a more general approach, whereby the model is trained on a generic dataset and tested on a dedicated AWE dataset. This challenging task is referred to as cross-domain few-shot semantic segmentation (CD-FSS). A novel model, namely FTDCNet, is proposed, which comprises a domain-agnostic feature transformation module and a domain-constrained transformer. The FTDCNet model demonstrates superior performance compared to the state-of-the-art (SOTA) model, with an accuracy improvement of 11.83% and 11.42% under 1-shot and 5-shot settings, respectively.