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公开(公告)号：US11335093B2
公开(公告)日：2022-05-17
申请号：US16966102
申请日：2019-06-12
Applicant: Google LLC
Inventor： Abhinav Shrivastava , Alireza Fathi , Sergio Guadarrama Cotado , Kevin Patrick Murphy , Carl Martin Vondrick
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing visual tracking. In one aspect, a method comprises receiving: (i) one or more reference video frames, (ii) respective reference labels for each of a plurality of reference pixels in the reference video frames, and (iii) a target video frame. The reference video frames and the target video frame are processed using a colorization machine learning model to generate respective pixel similarity measures between each of (i) a plurality of target pixels in the target video frame, and (ii) the reference pixels in the reference video frames. A respective target label is determined for each target pixel in the target video frame, comprising: combining (i) the reference labels for the reference pixels in the reference video frames, and (ii) the pixel similarity measures.
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公开(公告)号：US11163989B2
公开(公告)日：2021-11-02
申请号：US16637960
申请日：2019-08-06
Applicant: Google LLC
Inventor： Chen Sun , Abhinav Shrivastava , Cordelia Luise Schmid , Rahul Sukthankar , Kevin Patrick Murphy , Carl Martin Vondrick
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing action localization in images and videos. In one aspect, a system comprises a data processing apparatus; a memory in data communication with the data processing apparatus and storing instructions that cause the data processing apparatus to perform image processing and video processing operations comprising: receiving an input comprising an image depicting a person; identifying a plurality of context positions from the image; determining respective feature representations of each of the context positions; providing a feature representation of the person and the feature representations of each of the context positions to a context neural network to obtain relational features, wherein the relational features represent relationships between the person and the context positions; and determining an action performed by the person using the feature representation of the person and the relational features.
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公开(公告)号：US20210166009A1
公开(公告)日：2021-06-03
申请号：US16637960
申请日：2019-08-06
Applicant: Google LLC
Inventor： Chen Sun , Abhinav Shrivastava , Cordelia Luise Schmid , Rahul Sukthankar , Kevin Patrick Murphy , Carl Martin Vondrick
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing action localization. In one aspect, a system comprises a data processing apparatus; a memory in data communication with the data processing apparatus and storing instructions that cause the data processing apparatus to perform operations comprising: receiving an input comprising an image depicting a person; identifying a plurality of context positions from the image; determining respective feature representations of each of the context positions; providing a feature representation of the person and the feature representations of each of the context positions to a context neural network to obtain relational features, wherein the relational features represent relationships between the person and the context positions; and determining an action performed by the person using the feature representation of the person and the relational features.
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公开(公告)号：US20210089777A1
公开(公告)日：2021-03-25
申请号：US16966102
申请日：2019-06-12
Applicant: Google LLC
Inventor： Abhinav Shrivastava , Alireza Fathi , Sergio Guadarrama Cotado , Kevin Patrick Murphy , Carl Martin Vondrick
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing visual tracking. In one aspect, a method comprises receiving: (i) one or more reference video frames, (ii) respective reference labels for each of a plurality of reference pixels in the reference video frames, and (iii) a target video frame. The reference video frames and the target video frame are processed using a colorization machine learning model to generate respective pixel similarity measures between each of (i) a plurality of target pixels in the target video frame, and (ii) the reference pixels in the reference video frames. A respective target label is determined for each target pixel in the target video frame, comprising: combining (i) the reference labels for the reference pixels in the reference video frames, and (ii) the pixel similarity measures.
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