18090635. SYSTEMS AND METHODS FOR TRAINING VIDEO OBJECT DETECTION MACHINE LEARNING MODEL WITH TEACHER AND STUDENT FRAMEWORK simplified abstract (Robert Bosch GmbH)
Contents
- 1 SYSTEMS AND METHODS FOR TRAINING VIDEO OBJECT DETECTION MACHINE LEARNING MODEL WITH TEACHER AND STUDENT FRAMEWORK
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR TRAINING VIDEO OBJECT DETECTION MACHINE LEARNING MODEL WITH TEACHER AND STUDENT FRAMEWORK - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Object Detection with Teacher and Student Framework
- 1.13 Original Abstract Submitted
SYSTEMS AND METHODS FOR TRAINING VIDEO OBJECT DETECTION MACHINE LEARNING MODEL WITH TEACHER AND STUDENT FRAMEWORK
Organization Name
Inventor(s)
Tanvir Mahmud of Austin TX (US)
Chun-Hao Liu of Fremont CA (US)
Burhaneddin Yaman of San Jose CA (US)
SYSTEMS AND METHODS FOR TRAINING VIDEO OBJECT DETECTION MACHINE LEARNING MODEL WITH TEACHER AND STUDENT FRAMEWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 18090635 titled 'SYSTEMS AND METHODS FOR TRAINING VIDEO OBJECT DETECTION MACHINE LEARNING MODEL WITH TEACHER AND STUDENT FRAMEWORK
Simplified Explanation
This patent application describes a method for training an object-detection machine learning model using a teacher and student framework. The model is trained using a combination of labeled and unlabeled video data to improve semi-supervised video object detection.
Key Features and Innovation
- Utilizes pre-trained weights to initialize teacher and student models for object-detection machine learning.
- Teacher model generates pseudo-labels for unlabeled video data.
- Student model predicts pseudo-labels for unlabeled video data based on labeled data and pseudo-labels generated by the teacher model.
Potential Applications
This technology can be applied in various fields such as surveillance, autonomous vehicles, and robotics for improved object detection in videos.
Problems Solved
This technology addresses the challenge of training object-detection models with limited labeled data by leveraging unlabeled data in a semi-supervised manner.
Benefits
- Improved accuracy in video object detection.
- Efficient use of unlabeled data for training.
- Enhanced performance of object-detection models.
Commercial Applications
The technology can be utilized in industries such as security, transportation, and manufacturing for more accurate and reliable object detection in video streams.
Prior Art
Researchers can explore prior art related to semi-supervised learning in machine vision and object detection to understand the evolution of similar technologies.
Frequently Updated Research
Stay updated on advancements in semi-supervised learning techniques and applications in the field of computer vision for object detection.
Questions about Object Detection with Teacher and Student Framework
How does the teacher model generate pseudo-labels for unlabeled video data?
The teacher model generates pseudo-labels by leveraging the pre-trained weights and labeled video data to assign labels to the unlabeled video frames.
What are the potential commercial applications of this technology beyond video object detection?
This technology can also be applied in fields such as medical imaging, quality control in manufacturing, and environmental monitoring for enhanced object detection capabilities.
Original Abstract Submitted
Systems and methods for training an object-detection machine learning model with teacher and student framework. The training is intended to exploit a large number of unlabeled image or video frames with few labeled image or video frames for semi-supervised video object detection. For example, the object-detection machine learning model can be pre-trained based on labeled video data, and utilizing pre-trained weights, which initializes a teacher model and a student model with the pre-trained weights. The teacher model is trained to generate pseudo-labels for the unlabeled video data. The student model is trained to generated predicted pseudo-labels for the unlabeled video data, wherein the training of the student model is based on (i) the labeled video data and (ii) the pseudo-labels associated with the unlabeled video data.
