Micron technology, inc. (20240257531). TECHNIQUES TO IMPLEMENT TRANSFORMERS WITH MULTI-TASK NEURAL NETWORKS simplified abstract
Contents
TECHNIQUES TO IMPLEMENT TRANSFORMERS WITH MULTI-TASK NEURAL NETWORKS
Organization Name
Inventor(s)
Parth Khopkar of Seattle WA (US)
Shakti Nagnath Wadekar of West Lafayette IN (US)
Abhishek Chaurasia of Redmond WA (US)
Andre Xian Ming Chang of Bellevue WA (US)
TECHNIQUES TO IMPLEMENT TRANSFORMERS WITH MULTI-TASK NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240257531 titled 'TECHNIQUES TO IMPLEMENT TRANSFORMERS WITH MULTI-TASK NEURAL NETWORKS
Simplified Explanation: The patent application describes methods, systems, and devices for implementing transformers with multi-task neural networks in a machine learning system for object detection in images.
- Feature extractor uses convolutional layers to generate representation vectors of the image.
- Transformer models share a common input and generate indications of objects, drivable areas, and lane lines in the image.
- Multi-task system enhances efficiency by processing multiple tasks simultaneously.
Key Features and Innovation:
- Utilization of transformer models in a multi-task neural network for object detection.
- Feature extractor for generating representation vectors of images.
- Efficient processing of multiple tasks such as object detection, drivable areas, and lane line detection.
Potential Applications:
- Autonomous driving systems for real-time object detection.
- Surveillance systems for monitoring and tracking objects in images.
- Robotics for identifying and interacting with objects in the environment.
Problems Solved:
- Enhances efficiency in object detection tasks.
- Improves accuracy and speed of image analysis.
- Facilitates multi-task processing in machine learning systems.
Benefits:
- Increased accuracy in object detection.
- Faster processing of multiple tasks in images.
- Enhanced performance of machine learning systems.
Commercial Applications: Title: Multi-Task Neural Network for Object Detection in Images This technology can be applied in autonomous vehicles, surveillance systems, and robotics for efficient object detection and analysis in images, improving overall system performance and accuracy.
Prior Art: Researchers can explore prior art related to transformer models, multi-task neural networks, and object detection in images to understand the existing technologies and advancements in this field.
Frequently Updated Research: Researchers are constantly exploring new techniques and algorithms to improve object detection in images using neural networks and transformer models. Stay updated on recent developments in this area for the latest advancements in the field.
Questions about Multi-Task Neural Networks for Object Detection in Images: 1. How do transformer models enhance object detection in images compared to traditional neural networks? 2. What are the potential limitations of using multi-task neural networks for object detection in real-world applications?
Original Abstract Submitted
methods, systems, and devices for techniques to implement transformers with multi-task neural networks are described. a vehicle system may employ one or more transformer models in a machine learning system to generate an indication of a one or more objects in an image, one or more drivable areas in an image, one or more lane lines in an image, or a combination thereof. the multi-task system may include a feature extractor which uses a set of convolutional layers to generate a corresponding set of representation vectors of the image. the system may pass the representation vectors to a set of transformer models, such that each of the transformer models share a common input. each transformer model may use the representation vectors to generate a respective indication.
