Media Content Recognition via LLM-Generated Query Code
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Solution Overview
Problem
Existing vision language technologies require manual code writing by experts to process complex queries on media content, limiting their usability by non-expert users and increasing development time and costs.
Innovation Solution
A system that automates content recognition and custom computing code generation using large language models, allowing users to input queries without manual coding, leveraging keyword extraction, content detection models, and instructional language models to generate executable code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual code writing by experts is required to process complex queries on media content, then processing accuracy and reliability are improved, but ease of operation deteriorates and device complexity increases
Solution Approach 1:
The system enables non-expert users to automatically generate and execute code for processing media content queries without manual intervention. The code generation model creates executable code based on user queries, and the execution environment automatically runs the generated code, allowing users to benefit from expert-level processing capabilities without needing coding expertise themselves.
Solution Approach 2:
A code generation model acts as an intermediary between non-expert users and the complex code execution environment. The model translates user queries into executable code, bridging the gap between simple user input and sophisticated processing requirements, thereby improving ease of operation while maintaining processing accuracy.
2Reliability
If manual code writing by experts is required, then processing accuracy is improved, but productivity deteriorates due to increased development time
Solution Approach 1:
The system pre-trains the code generation model on extensive code datasets and execution environments before deployment. This preliminary training enables the model to generate accurate and executable code directly from user queries without requiring real-time expert intervention, significantly improving productivity while maintaining processing accuracy.
Solution Approach 2:
The automated code generation and execution system allows non-expert users to independently process complex media content queries without waiting for expert developers. The system self-generates and self-executes code based on user input, eliminating development bottlenecks and improving overall productivity.
3Adaptability or versatility
If manual code writing by experts is required, then adaptability to complex queries is improved, but device complexity increases
Solution Approach 1:
The code generation model is designed as a universal system that can handle diverse media content queries (image, video, audio) and generate appropriate code for various processing tasks. This multi-functional approach enables the system to adapt to different query types and complexities without requiring separate specialized systems, thereby improving adaptability while managing device complexity.
Solution Approach 2:
The code generation model serves as an intelligent intermediary that automatically adapts user queries into appropriate code for complex processing tasks. This intermediary handles the complexity of adapting to different query types and processing requirements, providing versatility without exposing the underlying system complexity to users.
Data Source
AI summary
Disclosed are apparatuses, systems, and techniques for automated content recognition with custom computing code generation using language models. The techniques include obtaining a first prompt for a description of a media item, processing, using a content detection model, the media item and a representation of the first prompt to obtain a characterization of the media item. The techniques further include generating, using the first prompt and the characterization of the media item, a second prompt that includes an instruction to a language model (LM). The techniques further include causing the LM to process the second prompt to generate a computing code associated with the characterization of the media item, and causing the computing code to be executed to generate the responsive description of the media item.