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公开(公告)号：US11200423B2
公开(公告)日：2021-12-14
申请号：US16687118
申请日：2019-11-18
Applicant: Google LLC
Inventor： Balakrishnan Varadarajan , George Dan Toderici , Apostol Natsev , Nitin Khandelwal , Sudheendra Vijayanarasimhan , Weilong Yang , Sanketh Shetty
Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method identifies an entity from a plurality of entities identifying characteristics of video items. The computer-implemented method selects a set of features correlated with the entity based on a value of a feature of a plurality of features, determines a classifier for the entity using the set of features, and determines an aggregation calibration function for the entity based on the set of features. The computer-implemented method selects a video frame from a video item, where the video frame having associated features, and determines a probability of existence of the entity based on the associated features using the classifier and the aggregation calibration function.
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公开(公告)号：US20210166035A1
公开(公告)日：2021-06-03
申请号：US17120525
申请日：2020-12-14
Applicant: Google LLC
Inventor： Sanketh Shetty , Tomas Izo , Min-Hsuan Tsai , Sudheendra Vijayanarasimhan , Apostol Natsev , Sami Abu-El-Haija , George Dan Toderici , Susana Ricco , Balakrishnan Varadarajan , Nicola Muscettola , WeiHsin Gu , Weilong Yang , Nitin Khandelwal , Phuong Le
IPC: G06K9/00 , G06F16/783
Abstract: A computer-implemented method for selecting representative frames for videos is provided. The method includes receiving a video and identifying a set of features for each of the frames of the video. The features including frame-based features and semantic features. The semantic features identifying likelihoods of semantic concepts being present as content in the frames of the video. A set of video segments for the video is subsequently generated. Each video segment includes a chronological subset of frames from the video and each frame is associated with at least one of the semantic features. The method generates a score for each frame of the subset of frames for each video segment based at least on the semantic features, and selecting a representative frame for each video segment based on the scores of the frames in the video segment. The representative frame represents and summarizes the video segment.
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公开(公告)号：US10740620B2
公开(公告)日：2020-08-11
申请号：US15782789
申请日：2017-10-12
Applicant: Google LLC
Inventor： Sudheendra Vijayanarasimhan , Alexis Bienvenu , David Ross , Timothy Novikoff , Arvind Balasubramanian
IPC: G06K9/00 , G06F3/0484 , G06N3/04
Abstract: A computer-implemented method includes receiving a video that includes multiple frames. The method further includes identifying a start time and an end time of each action in the video based on application of one or more of an audio classifier, an RGB classifier, and a motion classifier. The method further includes identifying video segments from the video that include frames between the start time and the end time for each action in the video. The method further includes generating a confidence score for each of the video segments based on a probability that a corresponding action corresponds to one or more of a set of predetermined actions. The method further includes selecting a subset of the video segments based on the confidence score for each of the video segments.
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公开(公告)号：US10482328B2
公开(公告)日：2019-11-19
申请号：US15722756
申请日：2017-10-02
Applicant: Google LLC
Inventor： Balakrishnan Varadarajan , George Dan Toderici , Apostol Natsev , Nitin Khandelwal , Sudheendra Vijayanarasimhan , Weilong Yang , Sanketh Shetty
Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method identifies an entity from a plurality of entities identifying characteristics of video items. The computer-implemented method selects a set of features correlated with the entity based on a value of a feature of a plurality of features, determines a classifier for the entity using the set of features, and determines an aggregation calibration function for the entity based on the set of features. The computer-implemented method selects a video frame from a video item, where the video frame having associated features, and determines a probability of existence of the entity based on the associated features using the classifier and the aggregation calibration function.
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公开(公告)号：US20180147723A1
公开(公告)日：2018-05-31
申请号：US15881189
申请日：2018-01-26
Applicant: Google LLC
Inventor： Sudheendra Vijayanarasimhan , Eric Jang , Peter Pastor Sampedro , Sergey Levine
CPC classification number: B25J9/163 , B25J9/1612 , B25J9/1697 , G05B13/027 , G05B19/18 , G06N3/008 , G06N3/0454 , G06N3/08 , G06N3/084 , Y10S901/36
Abstract: Deep machine learning methods and apparatus related to manipulation of an object by an end effector of a robot. Some implementations relate to training a semantic grasping model to predict a measure that indicates whether motion data for an end effector of a robot will result in a successful grasp of an object; and to predict an additional measure that indicates whether the object has desired semantic feature(s). Some implementations are directed to utilization of the trained semantic grasping model to servo a grasping end effector of a robot to achieve a successful grasp of an object having desired semantic feature(s).
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Patent Agency Ranking
公开(公告)号：US12141199B2
公开(公告)日：2024-11-12
申请号：US17548859
申请日：2021-12-13
Applicant: Google LLC
Inventor： Balakrishnan Varadarajan , George Dan Toderici , Apostol Natsev , Nitin Khandelwal , Sudheendra Vijayanarasimhan , Weilong Yang , Sanketh Shetty
IPC: G06K9/62 , G06F16/78 , G06F16/783 , G06F18/214 , G06F18/22 , G06F18/2413 , G06V20/40 , G06V20/70 , H04N5/265
Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method identifies an entity from a plurality of entities identifying characteristics of video items. The computer-implemented method selects a set of features correlated with the entity based on a value of a feature of a plurality of features, determines a classifier for the entity using the set of features, and determines an aggregation calibration function for the entity based on the set of features. The computer-implemented method selects a video frame from a video item, where the video frame having associated features, and determines a probability of existence of the entity based on the associated features using the classifier and the aggregation calibration function.
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公开(公告)号：US11763466B2
公开(公告)日：2023-09-19
申请号：US17132623
申请日：2020-12-23
Applicant: Google LLC
Inventor： Cordelia Luise Schmid , Sudheendra Vijayanarasimhan , Susanna Maria Ricco , Bryan Andrew Seybold , Rahul Sukthankar , Aikaterini Fragkiadaki
IPC: G06T7/269 , G06N3/02 , G06T3/40 , G06T9/00 , G06T7/215 , G06T7/70 , G06N3/045 , G06N3/048 , G06V10/82 , G06V10/44
CPC classification number: G06T7/269 , G06N3/045 , G06N3/048 , G06T7/215 , G06T7/70 , G06V10/454 , G06V10/82 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084
Abstract: A system comprising an encoder neural network, a scene structure decoder neural network, and a motion decoder neural network. The encoder neural network is configured to: receive a first image and a second image; and process the first image and the second image to generate an encoded representation of the first image and the second image. The scene structure decoder neural network is configured to process the encoded representation to generate a structure output characterizing a structure of a scene depicted in the first image. The motion decoder neural network configured to process the encoded representation to generate a motion output characterizing motion between the first image and the second image.
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公开(公告)号：US20220207873A1
公开(公告)日：2022-06-30
申请号：US17548859
申请日：2021-12-13
Applicant: Google LLC
Inventor： Balakrishnan Varadarajan , George Dan Toderici , Apostol Natsev , Nitin Khandelwal , Sudheendra Vijayanarasimhan , Weilong Yang , Sanketh Shetty
Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method identifies an entity from a plurality of entities identifying characteristics of video items. The computer-implemented method selects a set of features correlated with the entity based on a value of a feature of a plurality of features, determines a classifier for the entity using the set of features, and determines an aggregation calibration function for the entity based on the set of features. The computer-implemented method selects a video frame from a video item, where the video frame having associated features, and determines a probability of existence of the entity based on the associated features using the classifier and the aggregation calibration function.
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公开(公告)号：US20210118153A1
公开(公告)日：2021-04-22
申请号：US17132623
申请日：2020-12-23
Applicant: Google LLC
Inventor： Cordelia Luise Schmid , Sudheendra Vijayanarasimhan , Susanna Maria Ricco , Bryan Andrew Seybold , Rahul Sukthankar , Aikaterini Fragkiadaki
Abstract: A system comprising an encoder neural network, a scene structure decoder neural network, and a motion decoder neural network. The encoder neural network is configured to: receive a first image and a second image; and process the first image and the second image to generate an encoded representation of the first image and the second image. The scene structure decoder neural network is configured to process the encoded representation to generate a structure output characterizing a structure of a scene depicted in the first image. The motion decoder neural network configured to process the encoded representation to generate a motion output characterizing motion between the first image and the second image.
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公开(公告)号：US20180025228A1
公开(公告)日：2018-01-25
申请号：US15722756
申请日：2017-10-02
Applicant: Google LLC
Inventor： Balakrishnan Varadarajan , George Dan Toderici , Apostol Natsev , Nitin Khandelwal , Sudheendra Vijayanarasimhan , Weilong Yang , Sanketh Shetty
CPC classification number: G06K9/00718 , G06F16/783 , G06F16/7867 , G06K9/52 , G06K9/6201 , G06K9/6256 , G06K9/627 , H04N5/265
Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method identifies an entity from a plurality of entities identifying characteristics of video items. The computer-implemented method selects a set of features correlated with the entity based on a value of a feature of a plurality of features, determines a classifier for the entity using the set of features, and determines an aggregation calibration function for the entity based on the set of features. The computer-implemented method selects a video frame from a video item, where the video frame having associated features, and determines a probability of existence of the entity based on the associated features using the classifier and the aggregation calibration function.
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