View source for 18090635. SYSTEMS AND METHODS FOR TRAINING VIDEO OBJECT DETECTION MACHINE LEARNING MODEL WITH TEACHER AND STUDENT FRAMEWORK simplified abstract (Robert Bosch GmbH)

=SYSTEMS AND METHODS FOR TRAINING VIDEO OBJECT DETECTION MACHINE LEARNING MODEL WITH TEACHER AND STUDENT FRAMEWORK=


==Organization Name==

[[:Category:Robert Bosch GmbH|Robert Bosch GmbH]]


[[Category:Robert Bosch GmbH]]

==Inventor(s)==

[[:Category:Tanvir Mahmud of Austin TX (US)|Tanvir Mahmud of Austin TX (US)]][[Category:Tanvir Mahmud of Austin TX (US)]]

[[:Category:Chun-Hao Liu of Fremont CA (US)|Chun-Hao Liu of Fremont CA (US)]][[Category:Chun-Hao Liu of Fremont CA (US)]]

[[:Category:Burhaneddin Yaman of San Jose CA (US)|Burhaneddin Yaman of San Jose CA (US)]][[Category: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.



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