Canon kabushiki kaisha (20240289651). INFORMATION PROCESSING APPARATUS, IMAGE CAPTURING APPARATUS, METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM simplified abstract
INFORMATION PROCESSING APPARATUS, IMAGE CAPTURING APPARATUS, METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM
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INFORMATION PROCESSING APPARATUS, IMAGE CAPTURING APPARATUS, METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240289651 titled 'INFORMATION PROCESSING APPARATUS, IMAGE CAPTURING APPARATUS, METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM
Simplified Explanation: The patent application describes an information processing apparatus that determines tasks based on the results of inference from trained models detecting different targets.
- Trained models perform inference for detecting various targets on evaluation data.
- The apparatus combines tasks based on inference results to improve accuracy.
- Errors in detecting non-target objects lead to the determination of additional tasks.
Key Features and Innovation:
- Utilizes multiple trained models for detecting different targets.
- Combines tasks based on inference results to enhance accuracy.
- Addresses errors in detection by incorporating additional tasks.
Potential Applications: The technology can be applied in various fields such as image recognition, object detection, and data analysis.
Problems Solved: The technology addresses the challenge of accurately detecting multiple targets by combining tasks based on inference results.
Benefits:
- Improved accuracy in target detection.
- Enhanced efficiency in information processing.
- Reduction of errors in detection tasks.
Commercial Applications: The technology can be utilized in industries such as security, healthcare, and autonomous vehicles for improved target detection and data analysis.
Questions about the Technology: 1. How does the technology improve accuracy in target detection? 2. What are the potential applications of combining tasks based on inference results?
Frequently Updated Research: Stay updated on advancements in machine learning models for target detection and information processing.
Original Abstract Submitted
there is provided with an information processing apparatus. a first determining unit determines a first task from among a plurality of tasks based on a result of inference in the plurality of tasks, in which each of a plurality of trained models executing different tasks performs inference for detecting a different detection target on evaluation data. a second determining unit determines a second task to be combined with the first task based on a result of inference in which an object that is not a detection target of the first task was erroneously detected for evaluation data corresponding to the first task by a trained model executing the first task among the plurality of trained models.
