Qualcomm incorporated (20240378727). CONVOLUTION AND TRANSFORMER-BASED IMAGE SEGMENTATION simplified abstract

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CONVOLUTION AND TRANSFORMER-BASED IMAGE SEGMENTATION

Organization Name

qualcomm incorporated

Inventor(s)

Xin Li of San Diego CA (US)

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.