Mobile eye tracking is an important tool in psychology and humancentred interaction design for understanding how people process visual scenes and user interfaces. However, analysing recordings from mobile eye trackers, which typically include an egocentric video of the scene and a gaze signal, is a time-consuming and largely manual process. To address this challenge, we propose a web-based annotation tool that leverages few-shot image classification and interactive machine learning (IML) to accelerate the annotation process. The tool allows users to efficiently map fixations to areas of interest (AOI) in a video-editing-style interface. It includes an IML component that generates suggestions and learns from user feedback using a few-shot image classification model initialised with a small number of images per AOI. Our goal is to improve the efficiency and accuracy of fixation-to-AOI mapping in mobile eye tracking.

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