# PEANUT: A Human-AI Collaborative Tool for Annotating Audio-Visual Data

arXiv: https://arxiv.org/abs/2307.15167 · HTML: https://mlsanity.com/paper/2307.15167/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2023.

## Built on by (most cited first, 2 of 2)

- [Generate, Evaluate, Iterate: Synthetic Data for Human-in-the-Loop Refinement of LLM Judges](https://mlsanity.com/paper/2511.04478/) (2025)
- [ReFEree: Reference-Free and Fine-Grained Method for Evaluating Factual Consistency in Real-World Code Summarization](https://mlsanity.com/paper/2604.10520/) (2026)

## Abstract (arXiv)

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.