Automated Audio Description Generation Using Large Language Models and Tracking-based Character Recognition

Główne pojęcia

An automated pipeline that leverages the multimodal and instruction-following capabilities of GPT-4V to generate accurate audio descriptions for video content, complemented by a tracking-based character recognition module for consistent character information.

Streszczenie

The paper introduces an automated pipeline for generating audio descriptions (AD) for video content using GPT-4V, a large language model with advanced multimodal capabilities. The key aspects of the methodology are:

Character Recognition:

Employs a tracking-based approach to identify characters in the video, using face recognition and temporal information to maintain consistent character identities across frames.
This character recognition module operates without the need for additional training, ensuring generalizability to new video content.

Audio Description Generation:

Leverages the multimodal and instruction-following abilities of GPT-4V to generate AD that adhere to established production guidelines.
Integrates visual cues from video frames, textual context from subtitles, and character information to produce coherent and contextually relevant AD.
Allows direct control over the length of the generated AD by specifying desired word counts in the task prompts.

Evaluation and Benchmarking:

Extensive experiments on the MAD dataset demonstrate the effectiveness of the proposed approach, setting a new state-of-the-art performance with a CIDEr score of 20.5.
Ablation studies explore the impact of various components, such as visual prompts, textual context, and prompting strategies, on the quality of the generated AD.

The paper highlights the potential of large language models, particularly GPT-4V, in automating the production of audio descriptions for video content, while also introducing a novel tracking-based character recognition module to ensure consistent character information across frames.

Statystyki

The MAD dataset contains over 264,000 audio descriptions sourced from 488 movies.
The evaluation subset used in the experiments includes 10 carefully selected movies.

Cytaty

"Our methodology harnesses the multimodal capabilities of GPT-4V, integrating visual cues from video frames with textual context, such as previous subtitles, to generate AD content."
"By allowing the input of AD production guidelines and preferred output sentence lengths as natural language prompts, our system adeptly generates AD of suitable length tailored to speech gaps and can swiftly adapt to various video categories."

Kluczowe wnioski z

LLM-AD: Large Language Model based Audio Description System

by Peng Chu,Jia... o arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.00983.pdf

Głębsze pytania

How can the proposed method be extended to generate audio descriptions in multiple languages, catering to a more diverse audience?

To extend the proposed method for generating audio descriptions in multiple languages, several steps can be taken. Firstly, the training data for the language model can be augmented with multilingual datasets to enable the model to understand and generate content in different languages. Additionally, the prompts and instructions provided to the model can be translated into various languages to guide the generation of audio descriptions accurately. Moreover, incorporating language-specific nuances and cultural references in the training process can enhance the model's ability to produce contextually relevant descriptions in diverse languages. By fine-tuning the model on multilingual data and providing language-specific prompts, the system can cater to a broader audience by generating audio descriptions in multiple languages.

What are the potential challenges and limitations in applying this approach to live video streams or real-time video content, where the availability of textual context may be limited?

Applying the proposed approach to live video streams or real-time video content poses several challenges and limitations. One major challenge is the lack of pre-existing textual context in real-time scenarios, which can hinder the model's ability to generate accurate and contextually relevant audio descriptions. In live video streams, the rapid pace of events and the absence of subtitles or scripted dialogue can make it challenging for the model to capture and interpret the visual and auditory cues effectively. Additionally, the need for immediate processing and response in real-time scenarios may strain the computational resources and inference speed of the system, potentially leading to delays or inaccuracies in the generated descriptions. Ensuring the model's robustness and adaptability to dynamic and unpredictable live content poses a significant challenge in implementing this approach for real-time video streams.

Could the tracking-based character recognition module be further improved by incorporating additional cues, such as audio or dialogue information, to enhance its accuracy and robustness?

The tracking-based character recognition module can be enhanced by incorporating additional cues, such as audio or dialogue information, to improve its accuracy and robustness. By integrating audio analysis techniques, the system can leverage speech recognition to identify characters based on their voices or unique speech patterns. This audio information can complement the visual cues from the video frames, providing a more comprehensive and reliable method for character recognition. Furthermore, analyzing dialogue content and context can help disambiguate characters with similar visual appearances or movements, enhancing the model's ability to assign identities accurately. By fusing multiple modalities, including visual, audio, and textual information, the tracking-based character recognition module can achieve higher precision and consistency in identifying characters across frames, thereby improving the overall quality of the generated audio descriptions.