Dual Neural Network Recognition Model Alignment
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Solution Overview
Problem
Conventional neural network recognition models require excessive computational resources for processing sensor data, limiting the ability to improve recognition performance without increasing resource usage.
Innovation Solution
An information processing device that uses two neural network recognition models to process partial and entire sensor data, aligning intermediate outputs and generating integrated input data for subsequent processing, thereby reducing computational resource demands while enhancing recognition performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple neural network recognition models are used for processing sensor data, then recognition performance is improved, but computational resource consumption increases
Solution Approach 1:
The patent divides the sensor data processing into two segments: the first recognition model processes the entire sensor data to obtain first intermediate output data, while the second recognition model processes only the partial data (region of interest) to obtain second intermediate output data. This segmentation allows the system to maintain high recognition performance through dual-model processing while reducing computational resource consumption by limiting the second model's input to only the critical partial region rather than the complete sensor data set.
2Measurement precision
If multiple neural network recognition models are used for processing sensor data, then recognition performance is improved, but device complexity increases
Solution Approach 1:
The patent merges the outputs of two separate recognition models by aligning the second intermediate output data with the first intermediate output data based on spatial correspondence, then integrating them to generate final recognition results. This merging approach allows the system to leverage the strengths of both models (full-context understanding from the first model and detailed local analysis from the second model) while presenting a unified processing framework that manages system complexity through structured integration rather than independent parallel operations.
Data Source
AI summary
An information processing device includes: a processor configured to: obtain sensor data; determine partial data; input the sensor data to an input layer of a first neural network recognition model; input the partial data to an input layer of a second neural network recognition model; align second intermediate output data from a second intermediate layer of the second neural network recognition model, relative to first intermediate output data from a first intermediate layer of the first neural network recognition model; generate intermediate input data from the first intermediate output data and the second intermediate output data aligned; and input the intermediate input data to a third intermediate layer of the first neural network recognition model subsequent to the first intermediate layer.