Google llc (20240095927). Segmentation Models Having Improved Strong Mask Generalization simplified abstract
Contents
- 1 Segmentation Models Having Improved Strong Mask Generalization
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Segmentation Models Having Improved Strong Mask Generalization - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Segmentation Models Having Improved Strong Mask Generalization
Organization Name
Inventor(s)
Jonathan Chung-Kuan Huang of Seattle WA (US)
Vighnesh Nandan Birodkar of Montreal (CA)
Siyang Li of Sunnyvale CA (US)
Zhichao Lu of Santa Clara CA (US)
Vivek Rathod of Santa Clara CA (US)
Segmentation Models Having Improved Strong Mask Generalization - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095927 titled 'Segmentation Models Having Improved Strong Mask Generalization
Simplified Explanation
The computer-implemented method described in the abstract involves using a machine-learned segmentation model with an anchor-free detector model and a deep mask head network to segment images. The method includes obtaining input data, providing it to the segmentation model, and receiving output data with the segmented image.
- Machine-learned segmentation model with anchor-free detector model and deep mask head network
- Input data provided to the model for segmentation
- Output data received with segmented image including instance masks
Potential Applications
This technology can be applied in various fields such as medical imaging, autonomous vehicles, surveillance systems, and image editing software.
Problems Solved
This technology solves the problem of accurately segmenting images with improved strong mask generalization, even with partially supervised data.
Benefits
The benefits of this technology include more accurate image segmentation, improved generalization of masks, and the ability to work with partially supervised data.
Potential Commercial Applications
Potential commercial applications of this technology include developing image editing software, medical imaging tools, autonomous vehicle systems, and surveillance technologies.
Possible Prior Art
One possible prior art for this technology could be the use of deep learning models for image segmentation in various fields such as medical imaging and computer vision.
What are the limitations of this technology in real-world applications?
This technology may have limitations in terms of computational resources required for training and inference, as well as the need for large amounts of labeled data for training the segmentation model.
How does this technology compare to existing image segmentation methods?
This technology improves upon existing image segmentation methods by incorporating an anchor-free detector model and a deep mask head network for more accurate and generalized segmentation results.
Original Abstract Submitted
a computer-implemented method for partially supervised image segmentation having improved strong mask generalization includes obtaining, by a computing system including one or more computing devices, a machine-learned segmentation model, the machine-learned segmentation model including an anchor-free detector model and a deep mask head network, the deep mask head network including an encoder-decoder structure having a plurality of layers. the computer-implemented method includes obtaining, by the computing system, input data including tensor data. the computer-implemented method includes providing, by the computing system, the input data as input to the machine-learned segmentation model. the computer-implemented method includes receiving, by the computing system, output data from the machine-learned segmentation model, the output data including a segmentation of the tensor data, the segmentation including one or more instance masks.
