Samsung electronics co., ltd. (20240211749). METHOD AND APPARATUS WITH OBJECT ESTIMATION MODEL TRAINING simplified abstract
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METHOD AND APPARATUS WITH OBJECT ESTIMATION MODEL TRAINING
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METHOD AND APPARATUS WITH OBJECT ESTIMATION MODEL TRAINING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240211749 titled 'METHOD AND APPARATUS WITH OBJECT ESTIMATION MODEL TRAINING
The abstract describes a method and apparatus for training an object estimation model using neural networks.
- The method involves generating a cross-correlation loss based on two feature vectors from different neural networks.
- The first feature vector is generated by an interim neural network model using input data about a target object.
- The second feature vector is generated by a trained neural network using another set of input data about the same target object.
- The trained first neural network model is then generated by training the interim model based on the cross-correlation loss.
Potential Applications: - Object detection in image processing - Autonomous driving systems - Robotics and automation
Problems Solved: - Improving accuracy and efficiency of object estimation models - Enhancing the performance of neural networks in complex tasks
Benefits: - Higher precision in identifying and tracking objects - Faster processing speeds for real-time applications - Improved reliability and robustness in object recognition systems
Commercial Applications: "Enhanced Object Estimation Model Training for Advanced Image Processing and Robotics"
Frequently Updated Research: Stay updated on advancements in neural network training techniques for object estimation models.
Questions about Object Estimation Model Training: 1. How does the cross-correlation loss improve the training of neural networks for object estimation models? 2. What are the key differences between the interim and trained neural network models in this context?
Original Abstract Submitted
a method and apparatus with object estimation model training is provided. the method include generating a cross-correlation loss based on a first feature vector, generated using an interim first neural network (nn) model provided an input based on first input data about a target object, and a second feature vector generated using a trained second neural network provided another input based on second input data about the target object; and generating a trained first nn model, including training the interim first nn model based on the cross-correlation loss.
