Illumination-Guided progressive unsupervised domain adaptation for low-light instance segmentation.

Abstract

Due to limited photons, low-light environments pose significant challenges for computer vision tasks. Unsupervised domain adaptation offers a potential solution, but struggles with domain misalignment caused by inadequate utilization of features at different stages. To address this, we propose an Illumination-Guided Progressive Unsupervised Domain Adaptation method, called IPULIS, for low-light instance segmentation by progressively exploring the alignment of features at image-, instance-, and pixel-levels between normal- and low-light conditions under illumination guidance. This is achieved through: (1) an Illumination-Guided Domain Discriminator (IGD) for image-level feature alignment using retinex-derived illumination maps, (2) a Foreground Focus Module (FFM) incorporating global information with local center features to facilitate instance-level feature alignment, and (3) a Contour-aware Domain Discriminator (CAD) for pixel-level feature alignment by matching contour vertex features from a contour-based model. By progressively deploying these modules, IPULIS achieves precise feature alignment, leading to high-quality instance segmentation. Experimental results demonstrate that our IPULIS achieves state-of-the-art performance on real-world low-light dataset LIS.