  The Audio Description (AD) task aims to generate descriptions of visual
elements for visually impaired individuals to help them access long-form video
contents, like movie. With video feature, text, character bank and context
information as inputs, the generated ADs are able to correspond to the
characters by name and provide reasonable, contextual descriptions to help
audience understand the storyline of movie. To achieve this goal, we propose to
leverage pre-trained foundation models through a simple and unified framework
to generate ADs with interleaved multimodal sequence as input, termed as
Uni-AD. To enhance the alignment of features across various modalities with
finer granularity, we introduce a simple and lightweight module that maps video
features into the textual feature space. Moreover, we also propose a
character-refinement module to provide more precise information by identifying
the main characters who play more significant role in the video context. With
these unique designs, we further incorporate contextual information and a
contrastive loss into our architecture to generate more smooth and contextual
ADs. Experiments on the MAD-eval dataset show that Uni-AD can achieve
state-of-the-art performance on AD generation, which demonstrates the
effectiveness of our approach. Code will be available at
https://github.com/MCG-NJU/Uni-AD.
