AI Input Modification for Visual Media Relevancy
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Solution Overview
Problem
Existing AI models face challenges in generating relevant visual media outputs due to generic input descriptions lacking specificity, and the complexity of shared embedding spaces, which can result in irrelevant outputs and inefficient resource usage.
Innovation Solution
A system that modifies inputs for an AI model by identifying keywords and associating them with entity parameters, modifying the input to make keywords indicative of specific entities, and providing the modified input to the AI model to generate more relevant visual media outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If generic input descriptions are used for AI models, then the system is simple and easy to operate, but the relevancy of visual media outputs deteriorates
Solution Approach 1:
The system performs preliminary actions by identifying keywords and associating them with entity parameters before the AI model generates visual media outputs. This preprocessing step modifies the input to include entity-specific information, ensuring that the AI model receives enriched inputs that lead to more relevant outputs without requiring complex changes to the model itself.
Solution Approach 2:
The system introduces an intermediary component that sits between the generic input description and the AI model. This intermediary identifies keywords, associates them with entity parameters, and modifies the input accordingly. It acts as a mediator that transforms generic inputs into entity-specific inputs, improving output relevancy without directly modifying the AI model's internal structure.
2Productivity
If generic input descriptions are used for AI models, then the system uses fewer processing resources initially, but repeated inputs and irrelevant outputs increase overall resource consumption
Solution Approach 1:
The system implements feedback by using entity parameters identified from keywords to modify inputs and improve output relevancy. This feedback mechanism ensures that the AI model generates more accurate visual media outputs on the first attempt, reducing the need for repeated inputs and thereby improving overall resource efficiency and reducing time loss.
3Reliability
If keywords are modified to be indicative of specific entities, then the relevancy of outputs improves, but the complexity of input processing increases
Solution Approach 1:
The system applies segmentation by breaking down the input description into individual keywords and then modifying each keyword independently based on its association with entity parameters. This segmented approach to input modification allows for targeted enrichment of specific keywords without requiring complex overall input restructuring, thereby improving output relevancy while managing processing complexity.
Data Source
AI summary
In some implementations, a device may receive an input for the artificial intelligence model, wherein the input describes an output of the artificial intelligence model. The device may generate, via the artificial intelligence model, the output based on a modified input that is based on modifying one or more keywords included in the input to be indicative of respective entities based on the one or more keywords being associated with respective entity parameters. The device may provide, based on receiving the input, the output for display, wherein the output includes visual elements, associated with respective keywords of the one or more keywords, that indicate the respective entities.