View source for 18255186. Segmentation Models Having Improved Strong Mask Generalization simplified abstract (Google LLC)

=Segmentation Models Having Improved Strong Mask Generalization=


==Organization Name==

[[:Category:Google LLC|Google LLC]]


[[Category:Google LLC]]

==Inventor(s)==

[[:Category:Jonathan Chung-Kuan Huang of Seattle WA (US)|Jonathan Chung-Kuan Huang of Seattle WA (US)]][[Category:Jonathan Chung-Kuan Huang of Seattle WA (US)]]

[[:Category:Vighnesh Nandan Birodkar of Montreal (CA)|Vighnesh Nandan Birodkar of Montreal (CA)]][[Category:Vighnesh Nandan Birodkar of Montreal (CA)]]

[[:Category:Siyang Li of Sunnyvale CA (US)|Siyang Li of Sunnyvale CA (US)]][[Category:Siyang Li of Sunnyvale CA (US)]]

[[:Category:Zhichao Lu of Santa Clara CA (US)|Zhichao Lu of Santa Clara CA (US)]][[Category:Zhichao Lu of Santa Clara CA (US)]]

[[:Category:Vivek Rathod of Santa Clara CA (US)|Vivek Rathod of Santa Clara CA (US)]][[Category: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 18255186 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 masks.

* Machine-learned segmentation model with anchor-free detector and deep mask head network
* Input data provided to the model for segmentation
* Output data includes segmented masks

== Potential Applications ==
This technology could be applied in various fields such as medical imaging, autonomous vehicles, surveillance systems, and image editing software.

== Problems Solved ==
This technology helps in automating the process of image segmentation, making it more efficient and accurate. It also allows for partial supervision, reducing the need for manual labeling of large datasets.

== Benefits ==
The benefits of this technology include improved image segmentation accuracy, faster processing speed, and the ability to generalize well to new images without extensive training data.

== Potential Commercial Applications ==
Potential commercial applications of this technology include developing image editing software with advanced segmentation capabilities, integrating it into medical imaging systems for diagnostic purposes, and incorporating it into autonomous vehicles for object detection and tracking.

== Possible Prior Art ==
One possible prior art for this technology could be the use of deep learning models for image segmentation in various applications such as medical imaging and computer vision tasks.

=== What are the specific deep learning techniques used in the segmentation model described in the abstract? ===

The abstract mentions the use of an anchor-free detector model and a deep mask head network in the segmentation model. These techniques help in accurately detecting objects in images and generating instance masks for segmentation.

=== How does the anchor-free detector model improve the segmentation process compared to traditional methods? ===

The anchor-free detector model used in the segmentation model eliminates the need for predefined anchor boxes, allowing for more flexible and accurate object detection. This can lead to better segmentation results, especially in cases where objects vary significantly in size and shape.


==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.



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