AutoAD: Movie Description in Context (2303.16899v1)

Abstract: The objective of this paper is an automatic Audio Description (AD) model that ingests movies and outputs AD in text form. Generating high-quality movie AD is challenging due to the dependency of the descriptions on context, and the limited amount of training data available. In this work, we leverage the power of pretrained foundation models, such as GPT and CLIP, and only train a mapping network that bridges the two models for visually-conditioned text generation. In order to obtain high-quality AD, we make the following four contributions: (i) we incorporate context from the movie clip, AD from previous clips, as well as the subtitles; (ii) we address the lack of training data by pretraining on large-scale datasets, where visual or contextual information is unavailable, e.g. text-only AD without movies or visual captioning datasets without context; (iii) we improve on the currently available AD datasets, by removing label noise in the MAD dataset, and adding character naming information; and (iv) we obtain strong results on the movie AD task compared with previous methods.

Overview of "AutoAD: Movie Description in Context"

The paper "AutoAD: Movie Description in Context" by Tengda Han, Max Bain, Arsha Nagrani, Gül Varol, Weidi Xie, and Andrew Zisserman, presents an innovative approach to automatic movie audio description (AD) generation. The paper aims to bridge the gap between visual and linguistic models to produce high-quality AD, which is essential for visually impaired audiences to understand movies.

Research Problem

Generating movie AD is a challenging task due to the intricacies involved with context-dependent descriptions and the limited availability of high-quality training data. Traditional video captioning models struggle to capture the continuity and storytelling aspects required in movie AD, which involves understanding sequences rather than standalone visuals.

Methodology

To address these challenges, the authors leverage pretrained foundation models, such as GPT and CLIP, and design a mapping network that connects these models for visually-conditioned text generation. Their approach is composed of several key components:

1. Temporal Context Utilization: The model incorporates context from multiple sources including visual frames from movie clips, subtitles, and previous ADs. This is crucial for maintaining narrative consistency across scenes.
2. Pretraining on Diverse Datasets: Due to the limited availability of direct training data, the model is pretrained using large-scale datasets where either visual or contextual information is unavailable (e.g., text-only AD datasets, or visual captioning datasets without context).
3. Dataset Enhancement: The authors improve existing AD datasets, primarily the MAD dataset, by reducing label noise and including character naming information which is vital for narrative clarity.
4. Comparison and Results: AutoAD achieves notably high performance in generating movie AD when compared to existing methods. This is evaluated through a variety of metrics including ROUGE-L, CIDEr, and BertScore.

Numerical Findings and Implications

The paper conveys strong numerical results indicating that using temporal context significantly boosts the performance of AD generation systems. The approach demonstrates improved narrative coherence and a greater ability to capture the essence of cinematographic storytelling. Additionally, the method provides impressive zero-shot results on benchmarks such as the LSMDC multi-description benchmark, suggesting that large-scale LLMs can be powerful tools when their capabilities are augmented with appropriate design choices for AD tasks.

Practical and Theoretical Implications

The successful integration of GPT and CLIP for movie AD generation represents a significant progression in the field of AI, particularly in the domain of accessibility technologies. Practically, this research can lead to more engaging and informative movie experiences for visually impaired audiences. Theoretically, this work opens avenues for further exploration in multimodal representation learning, reinforcing the notion that context is a crucial component in visual-linguistic tasks.

Future Directions

Anticipated advancements may include refining character detection and naming techniques, further enhancing storytelling coherence. Additionally, exploring models that automatically determine when AD should be generated rather than relying on external annotations could lead to more autonomous systems. The continuous improvement of datasets by incorporating more comprehensive label sets and noise reduction techniques is also crucial for future research.

In summary, "AutoAD: Movie Description in Context" marks a significant step towards fully automated and contextually aware movie AD generation, capitalizing on advanced AI models and methodologies for a more inclusive cinematic experience.