Google Inc.

Mike Krainin

Ce Liu

Michael Rubinstein

Min-Hsuan Tsai

Yuanzhen Li

Andrei Kapishnikov

Wei-Hong Chuang

Marco Ziccardi

Rahul Sunil Bhalerao

Ye Xia

Derek Debusschere

Madhuri Shanbhogue

Mojtaba Seyedhosseini

A method includes obtaining first data including a first identifier of a first product determine in association with a content item based on first metadata of the content item. The method further includes obtaining a first confidence value associated with the first product and the content item. The method further includes obtaining second data including a second identifier of the first product and a second confidence value. The method further includes providing the first data and the second data to a trained machine learning model. The method further includes obtaining a third confidence value from the trained machine learning model associated with the first product. The method further includes adjusting second metadata of the content item in view of the third confidence value.

Product identification in media items

Example embodiments allow for fast, efficient motion-magnification of video streams by decomposing image frames of the video stream into local phase information at multiple spatial scales and/or orientations. The phase information for each image frame is then scaled to magnify local motion and the scaled phase information is transformed back into image frames to generate a motion-magnified video stream. Scaling of the phase information can include temporal filtering of the phase information across image frames, for example, to magnify motion at a particular frequency. In some embodiments, temporal filtering of phase information at a frequency of breathing, cardiovascular pulse, or some other process of interest allows for motion-magnification of motions within the video stream corresponding to the breathing or the other particular process of interest. The phase information can also be used to determine time-varying motion signals corresponding to motions of interest within the video stream.

Analysis and visualization of subtle motions in videos

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Mountain View, CA, United States of America

Mike Krainin