Estimation Model Adaptation for Specific Environments
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional domain adaptation technologies for machine learning models require user intervention and specific knowledge to adapt general estimation models to specific environments, leading to a high user burden and costs due to the need for manual input and assessment of adaptation success.
Innovation Solution
An information processing apparatus that automatically adapts a general estimation model to a specific environment using input images captured by an imaging apparatus, including an acquisition function to gather images, an adaptation function to adjust the model, and a determination function to assess adaptation success without user-specific knowledge, utilizing techniques like transfer learning and crowd analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a general estimation model is adapted to a specific environment using conventional domain adaptation technology, then the model can be customized for specific purposes, but user intervention and specific knowledge are required leading to high user burden and costs
Solution Approach 1:
The system automatically determines adaptation success by comparing estimation results before and after adaptation without requiring user assessment. The adaptation determination unit autonomously evaluates whether the adaptation process achieved the desired outcome, eliminating the need for user intervention in determining adaptation success.
Solution Approach 2:
The system performs preliminary estimation using the general model before adaptation, then compares it with post-adaptation estimation results. This preliminary action establishes a baseline that enables automatic determination of adaptation effectiveness without requiring user knowledge or manual assessment.
2Adaptability or versatility
If manual input of correct answer information is required for each image, then adaptation can be performed, but the cost and user burden increase significantly
Solution Approach 1:
The system uses the estimation result from the general model as a copy or baseline reference, then compares it with the adaptation result. By copying and comparing estimation outputs rather than requiring manual correct answer inputs, the system eliminates the need for extensive manual data entry while maintaining adaptation capability.
Solution Approach 2:
The system replaces the mechanical process of manual correct answer input with an automated computational process. Instead of users manually entering correct answers, the system uses algorithmic comparison of estimation results to determine adaptation success, substituting human manual work with automated processing.
3Measurement precision
If user assessment of adaptation state is required, then adaptation quality can be verified, but the process becomes complex and time-consuming
Solution Approach 1:
The system implements automatic feedback by comparing pre-adaptation and post-adaptation estimation results. The adaptation determination unit provides automated feedback on whether adaptation succeeded by analyzing the difference between estimation results, eliminating the need for complex manual assessment processes while maintaining verification accuracy.
Solution Approach 2:
The same estimation model and processing pipeline are used both before and after adaptation, making the assessment process universal. The system uses its core estimation functionality to assess adaptation success, rather than requiring separate complex assessment tools or user expertise, thereby simplifying the overall process while maintaining accuracy.
Data Source
AI summary
According to an embodiment, an information processing apparatus includes a memory and processing circuitry. The processing circuitry configured to acquire a plurality of input images captured at a specific place. The processing circuitry configured to adapt an estimation model used for detecting a target object included in images to the specific place based on the plurality of input images. The processing circuitry configured to output a result of determination of an adaptation state for the specific place in the estimation model.