LLM Video Understanding via Visual and Audio Prompt Generation
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Solution Overview
Problem
Conventional content item analysis techniques struggle to provide accurate and detailed descriptions of visual elements, audio components, and their interrelations within content items, often requiring significant computational resources and complex systems.
Innovation Solution
Utilizing large language models (LLMs) to analyze video frames, generating descriptive prompts based on visual and audio elements, and leveraging a knowledge base trained on a large corpus of data to output natural language descriptions of content items, reducing the need for complex machine learning and artificial intelligence systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional content item analysis techniques are used to analyze visual elements and audio components, then detailed descriptions can be provided, but significant computational resources and complex systems are required
Solution Approach 1:
The patent segments the video content into individual frames and processes each frame independently through the LLM. Visual elements, audio components, and their interrelations are analyzed separately and then integrated to form a complete description, reducing the complexity of processing the entire video at once while maintaining detailed analysis accuracy
Solution Approach 2:
The patent introduces an intermediary processing layer that converts visual and audio data into textual representations that the LLM can process. This intermediary step translates complex multimedia data into a format suitable for language model processing, reducing the complexity of directly analyzing raw video and audio streams while preserving detailed information
2Measurement precision
If conventional content item analysis techniques are used to analyze visual elements and audio components, then detailed descriptions can be provided, but significant computational resources are required
Solution Approach 1:
The patent applies partial action by selecting and processing only the most relevant visual and audio elements in each frame rather than analyzing every aspect of the content. The LLM focuses on key relationships and significant elements, reducing computational resource consumption while maintaining accurate and detailed descriptions of the content item
Solution Approach 2:
The patent extracts essential visual and audio features from each frame and feeds only these extracted elements to the LLM for processing. By taking out only the necessary information rather than processing the complete raw data, the system reduces computational resource requirements while preserving the accuracy needed for detailed content descriptions
Data Source
AI summary
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for deep video understanding with large language models. An example embodiment operates by determining a relationship between respective first and second visual elements for each of a plurality of frames of a content item based on respective element types and respective locations for the respective first and second visual elements. For each of the plurality of frames, a respective visual prompt is generated describing the relationship between the respective first and second visual elements. Based on an audio-to-text conversion of audio content associated with the frame or classification of aural elements of the audio content, a respective audio prompt describing the audio content associated with each frame is generated. A description of the content item is output by a large language model (LLM) based on the visual prompts and audio prompts for the plurality of frames input to the LLM.