LLM-AD: Large Language Model based Audio Description System (2405.00983v1)

Abstract: The development of Audio Description (AD) has been a pivotal step forward in making video content more accessible and inclusive. Traditionally, AD production has demanded a considerable amount of skilled labor, while existing automated approaches still necessitate extensive training to integrate multimodal inputs and tailor the output from a captioning style to an AD style. In this paper, we introduce an automated AD generation pipeline that harnesses the potent multimodal and instruction-following capacities of GPT-4V(ision). Notably, our methodology employs readily available components, eliminating the need for additional training. It produces ADs that not only comply with established natural language AD production standards but also maintain contextually consistent character information across frames, courtesy of a tracking-based character recognition module. A thorough analysis on the MAD dataset reveals that our approach achieves a performance on par with learning-based methods in automated AD production, as substantiated by a CIDEr score of 20.5.

• The paper presents an automated audio description pipeline using GPT-4V that integrates multimodal processing, natural language instructions, and character tracking.
• It employs a methodology that combines visual inputs and subtitles to generate descriptive audio synchronized with video action.
• The system achieves a CIDEr score of 20.5 on the MAD dataset, indicating significant improvements in accessible video content generation.

Exploring Automated Audio Description (AD) Generation with GPT-4V(ision)

Introduction to Audio Description (AD)

Audio Description (AD) is an essential tool for making video content accessible to individuals with visual impairments. It involves providing a spoken narrative of visual elements in a video, such as settings, facial expressions, and actions, which are not covered by the dialogues. Producing AD is traditionally labor-intensive, requiring expert human annotators who can create narratives that align with the action on screen seamlessly. The paper discussed introduces an innovative automated pipeline leveraging GPT-4V, a large multimodal model, to generate AD without the need for intensive manual effort or extensive dataset-specific training.

The GPT-4V(ision) Approach

The core of the proposed methodology is GPT-4V(ision), an iteration of LLMs that integrates capabilities to process both text and visual inputs to generate textual outputs. This model allows for the incorporation of AD production guidelines directly into the generation process, using them as prompts that instruct the model to produce AD in a style and length fitting the pauses in dialogue within a video.

Key features of the GPT-4V approach include:

• Multimodal Input Processing: Integration of visual and textual inputs from video frames and subtitles.
• Natural Language Instructions: Utilize instructions to guide AD generation, specifying style and length.
• Character Consistency Across Frames: A tracking-based character recognition module that requires no additional training and can identify characters consistently across different frames.

Methodology Breakdown

The approach involves several components that work together to generate AD:

1. Character Recognition: The system identifies characters using a tracking module that analyzes the video frames sequentially, ensuring that characters are recognized even if the camera angle changes or if there are multiple people in the scene. This is done using existing databases for basic character information combined with dynamic tracking to adapt to each specific video.
2. AD Content Generation: Leveraging the multimodal capabilities of GPT-4V, the system processes both the visual content of frames and the associated textual content like movie titles or subtitles. This combined data, augmented with character tracking information, is fed into GPT-4V as a structured prompt that includes instructions on the desired output style and length.

Experimentation and Results

The system was tested on the MAD dataset, which includes hundreds of movies with pre-existing AD for benchmarking. The automated AD generation was evaluated using metrics like CIDEr, where the system achieved a score of 20.5, demonstrating its effectiveness compared to other learning-based methods for AD generation.

• Character Recognition Efficiency: The tracking-based approach allowed for higher recall in character tracking compared to solely recognition-based methods, which is crucial for maintaining narrative consistency in AD.
• Handling of Visual and Textual Prompts: The system effectively integrated visual cues (like character bounding boxes and names) with textual context to provide a rich input for GPT-4V, enhancing the relevance and coherence of the generated AD.

Implications and Future Directions

Automating AD generation using models like GPT-4V(ision) can drastically reduce the resources needed to make video content accessible. Such advancements could not only benefit individuals with visual impairments but also enhance the viewing experience for broader audiences who might enjoy content in an "eyes-free" manner.

Future enhancements could include:

• Improved Contextual Timing: Developing methods to automatically determine optimal times for inserting AD based on video context and dialogue pauses.
• Adaptability to Various Content Types: Extending the system to different genres and styles of video content, which may have varying narrative pacing and visual elements.

The advent of tools like GPT-4V represents a significant potential for making video content more inclusive on a large scale, pointing toward a future where video accessibility can be widely available without prohibitive costs or specialized labor.