Kevin Lin

US Patent:

20220405550, Dec 22, 2022

Filed:

Aug 24, 2022

Appl. No.:

17/822080

Inventors:

- Redmond WA, US
Kevin LIN - Seattle WA, US
Zicheng LIU - Bellevue WA, US
Kun LUO - Redmond WA, US

Assignee:

Microsoft Technology Licensing, LLC - Redmond WA

International Classification:

G06N 3/04
G06N 3/08
G06T 7/10
G06F 17/15
G06V 40/10

Abstract:

A computing system is provided. The computing system includes a processor configured to execute a convolutional neural network that has been trained, the convolutional neural network including a backbone network that is a concatenated pyramid network, a plurality of first head neural networks, and a plurality of second head neural networks. At the backbone network, the processor is configured to receive an input image as input and output feature maps extracted from the input image. The processor is configured to: process the feature maps using each of the first head neural networks to output corresponding keypoint heatmaps; process the feature maps using each of the second head neural networks to output corresponding part affinity field heatmaps; link the keypoints into one or more instances of virtual skeletons using the part affinity fields; and output the instances of the virtual skeletons.

US Patent:

20200272812, Aug 27, 2020

Filed:

Feb 21, 2019

Appl. No.:

16/281876

Inventors:

- Redmond WA, US
Zicheng Liu - Bellevue WA, US
Kevin Lin - Redmond WA, US
Kun Luo - Redmond WA, US

International Classification:

G06K 9/00
G06K 9/62

Abstract:

A machine accesses a training data set comprising multiple real images and multiple synthetic images. The machine trains a joint prediction module to predict joint locations in visual data using the multiple real images. The machine trains a part affinity field prediction module to identify adjacent joints in visual data using the multiple real images. The machine trains the joint prediction module to predict joint locations in visual data using the multiple synthetic images. The machine trains the part affinity field prediction module to identify adjacent joints in visual data using the multiple synthetic images. The machine trains a body part prediction module to identify body parts in visual data using the multiple synthetic images. The machine provides a trained human body part segmentation module comprising the trained joint prediction module, the trained part affinity field prediction module, and the trained body part prediction module.

US Patent:

20200272888, Aug 27, 2020

Filed:

Apr 26, 2019

Appl. No.:

16/396513

Inventors:

- Redmond WA, US
Kevin LIN - Seattle WA, US
Zicheng LIU - Bellevue WA, US
Kun LUO - Redmond WA, US

Assignee:

Microsoft Technology Licensing, LLC - Redmond WA

International Classification:

G06N 3/04
G06N 3/08
G06T 7/10
G06K 9/00
G06F 17/15

Abstract:

A computing system is provided. The computing system includes a processor configured to execute a convolutional neural network that has been trained, the convolutional neural network including a backbone network that is a concatenated pyramid network, a plurality of first head neural networks, and a plurality of second head neural networks. At the backbone network, the processor is configured to receive an input image as input and output feature maps extracted from the input image. The processor is configured to: process the feature maps using each of the first head neural networks to output corresponding keypoint heatmaps; process the feature maps using each of the second head neural networks to output corresponding part affinity field heatmaps; link the keypoints into one or more instances of virtual skeletons using the part affinity fields; and output the instances of the virtual skeletons.