Microsoft Technology Licensing, LLC

Ying Jin

Zicheng Liu

Yinpeng Chen

Kun Luo

Hongli Deng

Baoyuan Wang

Noranart Vesdapunt

Pei Qun Yu

Lijuan Wang

Pei Yu

Pierrick Arsenault

A method for environment-specific training of a machine learning model, comprises receiving, for a local environment, a data stream including a plurality of sequential data snippets. Programmed labels are generated for each data snippet using a student version of a machine learning model. A portion of data snippets and associated programmed labels are selected and uploaded to a server-side computing device for evaluation by a teacher version of the machine learning model. An environment-specific training update is received from the server-side computing device. This training update is based on a comparison of the selected programmed labels and pseudolabels generated for the selected portion of data snippets by the teacher version. The environment-specific training update is applied to the student version to generate an updated student version. The updated student version of the machine learning model is then used to generate programmed labels for newly received data snippets.

Environment-specific training of machine learning models

To improve the accuracy and efficiency of object detection through computer digital image analysis, the detection of some objects can inform the sub-portion of the digital image to which subsequent computer digital image analysis is directed to detect other objects. In such a manner object detection can be made more efficient by limiting the image area of a digital image that is analyzed. Such efficiencies can represent both computational efficiencies and communicational efficiencies arising due to the smaller quantity of digital image data that is analyzed. Additionally, the detection of some objects can render the detection of other objects more accurate by adjusting confidence thresholds based on the detection of those related objects. Relationships between objects can be utilized to inform both the image area on which subsequent object detection is performed and the confidence level of such subsequent object detection.

Focused computer detection of objects in images

Described herein is a human pose prediction system and method. An image comprising at least a portion of a human body is received. A trained neural network is used to predict one or more human features (e.g., joints/aspects of a human body) within the received image, and, to predict one or more human poses in accordance with the predicted one or more human features. The trained neural network can be an end-to-end trained, single stage deep neural network. An action is performed based on the predicted one or more human poses. For example, the human pose(s) can be displayed as an overlay with received image.

Human pose estimation

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Ying Jin