Samsung electronics co., ltd. (20240161442). METHOD AND APPARATUS WITH OBJECT DETECTOR TRAINING simplified abstract
Contents
- 1 METHOD AND APPARATUS WITH OBJECT DETECTOR TRAINING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND APPARATUS WITH OBJECT DETECTOR TRAINING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
METHOD AND APPARATUS WITH OBJECT DETECTOR TRAINING
Organization Name
Inventor(s)
Gyusam Chang of Seongnam-si (KR)
METHOD AND APPARATUS WITH OBJECT DETECTOR TRAINING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240161442 titled 'METHOD AND APPARATUS WITH OBJECT DETECTOR TRAINING
Simplified Explanation
The abstract describes a method and apparatus for object detector training, involving obtaining input data from a target object, performing data augmentation, extracting features to a shared embedding space, identifying loss functions, and updating weights of an encoder.
- Obtaining input data from a target object
- Performing data augmentation on the input data
- Extracting features to a shared embedding space
- Identifying loss functions based on the extracted features
- Updating weights of the encoder based on the loss functions
Potential Applications
This technology can be applied in various fields such as computer vision, autonomous driving, robotics, and surveillance systems for object detection and recognition tasks.
Problems Solved
This technology helps improve the accuracy and efficiency of object detection systems by training the detector with augmented data and shared embedding spaces, leading to better feature extraction and loss function identification.
Benefits
The benefits of this technology include enhanced object detection performance, increased robustness to variations in input data, and improved generalization capabilities for detecting different types of objects in various environments.
Potential Commercial Applications
Potential commercial applications of this technology include implementing advanced object detection systems in security cameras, industrial automation, retail analytics, and smart city infrastructure for real-time monitoring and analysis.
Possible Prior Art
One possible prior art for this technology could be the use of data augmentation techniques in machine learning models for improving performance and generalization capabilities. Additionally, shared embedding spaces have been utilized in various deep learning applications for feature extraction and representation learning.
Unanswered Questions
How does this technology compare to existing object detector training methods in terms of accuracy and efficiency?
This article does not provide a direct comparison with existing methods, so it is unclear how this technology performs in relation to other approaches.
What are the computational requirements and training times for implementing this object detector training method?
The abstract does not mention the computational resources or training times needed for this technology, leaving a gap in understanding the practical implications of its implementation.
Original Abstract Submitted
a method and apparatus with object detector training is provided. the method includes obtaining first input data and second input data from a target object; obtaining second additional input data by performing data augmentation on the second input data; extracting a first feature to a shared embedding space by inputting the first input data to a first encoder; extracting a second feature to the shared embedding space by inputting the second input data to a second encoder; extracting a second additional feature to the shared embedding space by inputting thesecond additional input data to the second encoder; identifying a first loss function based on the first feature, the second feature, and the second additional feature; identifying a second loss function based on the second feature and the second additional feature; and updating a weight of the second encoder based on the first loss function and the second loss function.
