Audio-visual learning seeks to enhance the computer's multi-modal perception leveraging the correlation between the auditory and visual modalities. Despite their many useful downstream tasks, such as video retrieval, AR/VR, and accessibility, the performance and adoption of existing audio-visual models have been impeded by the availability of high-quality datasets. Annotating audio-visual datasets is laborious, expensive, and time-consuming. To address this challenge, we designed and developed an efficient audio-visual annotation tool called Peanut. Peanut's human-AI collaborative pipeline separates the multi-modal task into two single-modal tasks, and utilizes state-of-the-art object detection and sound-tagging models to reduce the annotators' effort to process each frame and the number of manually-annotated frames needed. A within-subject user study with 20 participants found that Peanut can significantly accelerate the audio-visual data annotation process while maintaining high annotation accuracy.
@article{arxiv.2307.15167,
title = {PEANUT: A Human-AI Collaborative Tool for Annotating Audio-Visual Data},
author = {Zheng Zhang and Zheng Ning and Chenliang Xu and Yapeng Tian and Toby Jia-Jun Li},
journal= {arXiv preprint arXiv:2307.15167},
year = {2023}
}