Dual Inference Model for Offline Image Analysis
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Solution Overview
Problem
Information processing apparatuses with limited processing capability, such as digital cameras or mobile terminals, face challenges in analyzing image data accurately and efficiently due to their inability to meet the high processing requirements for learning and inference, especially when communication with external devices is disrupted.
Innovation Solution
The apparatus is equipped with both a simple inference model for offline analysis and a transmission unit to send data to an external device for detailed analysis when connected, allowing it to acquire analysis results even in offline conditions by using a less accurate second inference model for image data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data learning and analysis is performed by an external device with high processing capability, then analysis accuracy is improved, but the system cannot operate when network communication is disconnected
Solution Approach 1:
The patent segments the inference model into two versions: a first inference model with high accuracy stored in the external device, and a second inference model with lower accuracy stored in the information processing apparatus. This segmentation allows the system to use the appropriate model based on network availability, resolving the contradiction between accuracy and reliability.
Solution Approach 2:
The patent performs preliminary action by storing the second inference model in advance in the information processing apparatus. This allows the device to immediately switch to offline mode and perform image analysis without delay when network communication is disconnected, ensuring continuous operation.
2Measurement precision
If a high processing capability apparatus is used for image data learning, then analysis accuracy is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent applies local quality by having different inference models with different accuracy levels deployed in different locations. The high-accuracy first inference model resides in the external device, while the lower-accuracy second inference model is placed in the information processing apparatus. This allows each location to have the processing capability appropriate for its specific needs and constraints.
Solution Approach 2:
The patent uses copying by creating a second inference model that is a simplified version or copy of the first inference model. This copied model can be stored and executed in devices with limited processing capability, allowing accurate analysis when connected to the network while maintaining operational capability when disconnected.
3Measurement precision
If a more accurate first inference model is used, then analysis precision is improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements dynamics by making the inference model selection dynamic based on network availability. The system can switch between the first inference model (high accuracy, longer processing time) when the external device is accessible, and the second inference model (lower accuracy, shorter processing time) when operating offline. This dynamic adaptation resolves the contradiction between precision and processing time.
Data Source
AI summary
An information processing apparatus capable of communicating with an external device includes an analysis unit configured to analyze image data and to acquire a second analysis result using a second inference model that is less accurate than a first inference model of an external device when communication with the external device is not possible, a transmission unit configured to transmit the image data to the external device when communication with the external device is possible, and an acquisition unit configured to acquire, from the external device, a first analysis result obtained by analyzing, using the first inference model, the image data transmitted to the external device by the transmission unit. The first inference model and the second inference model are generated by performing machine learning.