Qualcomm incorporated (20240378727). CONVOLUTION AND TRANSFORMER-BASED IMAGE SEGMENTATION simplified abstract
Contents
CONVOLUTION AND TRANSFORMER-BASED IMAGE SEGMENTATION
Organization Name
Inventor(s)
Jiancheng Lyu of San Diego CA (US)
Yingyong Qi of San Diego CA (US)
CONVOLUTION AND TRANSFORMER-BASED IMAGE SEGMENTATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240378727 titled 'CONVOLUTION AND TRANSFORMER-BASED IMAGE SEGMENTATION
Simplified Explanation: The patent application describes techniques for image processing, including extracting features at different scale resolutions, performing self-attention and cross-attention transforms, and generating instance masks.
Key Features and Innovation:
- Extracting features at different scale resolutions
- Performing self-attention and cross-attention transforms
- Generating instance masks for image processing
Potential Applications: This technology can be applied in various fields such as computer vision, image recognition, and object detection.
Problems Solved: This technology addresses the need for efficient and effective image processing techniques that can extract features at different scales and generate instance masks.
Benefits:
- Improved image processing accuracy
- Enhanced feature extraction capabilities
- Efficient object detection and recognition
Commercial Applications: Potential commercial applications include automated surveillance systems, medical imaging analysis, and autonomous driving technology.
Prior Art: Researchers can explore prior art related to image processing, feature extraction, and attention mechanisms in the field of computer vision.
Frequently Updated Research: Stay updated on the latest advancements in image processing, feature extraction, and attention mechanisms to enhance the application of this technology.
Questions about Image Processing: 1. How does this technology improve feature extraction in image processing? 2. What are the potential limitations of using self-attention and cross-attention transforms in image processing?
Original Abstract Submitted
techniques are provided for image processing. for instance, a process can include obtaining an image; extracting a first set of features at a first scale resolution; extracting a second set of features at a second scale resolution (lower than the first scale resolution); performing a self-attention transform to generate similarity scores for the second set of features; adding the similarity scores to the second set of features to generate a first feature extractor output; up-sampling the first feature extractor output to generate a second feature extractor output; adding the second feature extractor output to the first set of features to generate a third feature extractor output; receiving an instance query; performing a cross-attention transform on the instance query and the first feature extractor output to generate a set of weights; and matrix multiplying the set of weights and the third feature extractor output to generate instance masks.
