Open-Vocabulary Object Recognition Prompt Selection by Semantic Similarity
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Solution Overview
Problem
The accuracy of open vocabulary machine learning models in object recognition is heavily dependent on terminology, leading to inconsistent recognition rates due to synonyms and requiring manual selection, which is costly, time-consuming, and prone to errors.
Innovation Solution
An automated method for generating a set of predefined text descriptions by selecting the most similar encoded dictionary text descriptions to initial text descriptions using semantic similarity measures, reducing the need for manual prompt engineering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of terminology is performed during prompt engineering, then recognition accuracy can be optimized, but costs and time expenditure increase significantly
Solution Approach 1:
The system automatically generates and selects optimal text descriptions using the machine learning model itself, eliminating the need for manual prompt engineering. The model encodes images, compares encoded representations against a dictionary, and selects the most similar text descriptions automatically, making the system self-sufficient in optimizing its own terminology.
Solution Approach 2:
The manual mechanical process of prompt engineering is replaced by an automated computational system. The machine learning model uses encoded image representations and similarity measures to automatically select optimal text descriptions, substituting human manual work with an automated algorithmic process.
2Measurement precision
If manual selection of terminology is performed during prompt engineering, then recognition accuracy can be optimized, but personnel costs increase
Solution Approach 1:
The system performs its own prompt engineering automatically without requiring human specialists. The machine learning model independently encodes images, compares them against the dictionary, and selects optimal text descriptions, eliminating the need for paid personnel to perform manual prompt engineering.
Solution Approach 2:
The expensive manual process involving personnel costs is replaced by an automated computational system that uses encoded image representations and similarity comparisons to select text descriptions, converting human labor costs into computational processing.
3Ease of operation
If a limited vocabulary is used due to operator's language barriers, then manual prompt engineering becomes simpler, but performance degradation occurs
Solution Approach 1:
The system replaces manual human judgment about vocabulary selection with an automated computational process. The machine learning model objectively compares encoded image representations against the full dictionary using similarity measures, eliminating biases and limitations imposed by human language barriers while maintaining operational simplicity.
4Productivity
If automated text description selection is implemented, then manual costs and time are reduced, but system complexity increases
Solution Approach 1:
The machine learning model performs multiple functions: it encodes images, compares encoded representations against the dictionary, and selects optimal text descriptions. This multi-functionality consolidates what would otherwise require separate manual steps into a single automated process, managing complexity through functional integration.
Solution Approach 2:
The encoded image representation serves as an intermediary between the input image and the text description selection process. By converting images into a common encoded space that can be compared against dictionary entries, the system creates a standardized intermediate representation that simplifies the overall process despite the automated nature.
Data Source
AI summary
A method for generating predefined text descriptions for a trained open vocabulary machine learning model. The method includes: providing images and initial text descriptions, each associated with a region in a corresponding image and indicating what is shown in the region; ascertaining encoded dictionary text descriptions using a text encoder of the learning model; for each initial text description: ascertaining an encoded initial text description using the text encoder, selecting encoded dictionary text description(s) most similar to the encoded initial text description, inputting the image associated with the initial text description into the machine learning model and ascertaining a similarity between an output of the machine learning model and the region associated with the initial text description, and adding the text description with the greatest similarity to the set of predefined text descriptions.