Generating Audio Description (AD) for movies is a challenging task that requires fine-grained visual understanding and an awareness of the characters and their names. Currently, visual language models for AD generation are limited by a lack of suitable training data, and also their evaluation is hampered by using performance measures not specialized to the AD domain. In this paper, we make three contributions: (i) We propose two approaches for constructing AD datasets with aligned video data, and build training and evaluation datasets using these. These datasets will be publicly released; (ii) We develop a Q-former-based architecture which ingests raw video and generates AD, using frozen pre-trained visual encoders and large language models; and (iii) We provide new evaluation metrics to benchmark AD quality that are well-matched to human performance. Taken together, we improve the state of the art on AD generation.
@article{arxiv.2404.14412,
title = {AutoAD III: The Prequel -- Back to the Pixels},
author = {Tengda Han and Max Bain and Arsha Nagrani and Gül Varol and Weidi Xie and Andrew Zisserman},
journal= {arXiv preprint arXiv:2404.14412},
year = {2024}
}