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公开(公告)号：US20240126365A1
公开(公告)日：2024-04-18
申请号：US18279117
申请日：2021-04-21
Applicant: Google LLC
Inventor： Dmitry Lagun , Gautam Prasad , Pezhman Firoozfam , Jimin Pi
CPC classification number: G06F3/013 , G06T7/70 , G06T11/60 , G06T2207/20081 , G06T2207/20084
Abstract: The technology relates to methods and systems for implicit calibration for gaze tracking. This can include receiving, by a neural network module, display content that is associated with presentation on a display screen (1202). The neural network module may also receive uncalibrated gaze information, in which the uncalibrated gaze information includes an uncalibrated gaze trajectory that is associated with a viewer gaze of the display content on the display screen (1204). A selected function is applied by the neural network module to the uncalibrated gaze information and the display content to generate a user-specific gaze function (1206). The user-specific gaze function has one or more personalized parameters. And the neural network module can then apply the user-specific gaze function to the uncalibrated gaze information to generate calibrated gaze information associated with the display content on the display screen (1208). Training and testing information may alternatively be created for implicit gaze calibration (1000).
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公开(公告)号：US11881022B2
公开(公告)日：2024-01-23
申请号：US18181806
申请日：2023-03-10
Applicant: Google LLC
Inventor： Ting Liu , Gautam Prasad , Phuc Xuan Nguyen , Bohyung Han
IPC: G06V20/40 , G06F18/214 , G06F18/243
CPC classification number: G06V20/40 , G06F18/214 , G06F18/24317 , G06V20/44
Abstract: Systems and methods for a weakly supervised action localization model are provided. Example models according to example aspects of the present disclosure can localize and/or classify actions in untrimmed videos using machine-learned models, such as convolutional neural networks. The example models can predict temporal intervals of human actions given video-level class labels with no requirement of temporal localization information of actions. The example models can recognize actions and identify a sparse set of keyframes associated with actions through adaptive temporal pooling of video frames, wherein the loss function of the model is composed of a classification error and a sparsity of frame selection. Following action recognition with sparse keyframe attention, temporal proposals for action can be extracted using temporal class activation mappings, and final time intervals can be estimated corresponding to target actions.
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Patent Agency Ranking
公开(公告)号：US11640710B2
公开(公告)日：2023-05-02
申请号：US16625172
申请日：2018-11-05
Applicant: Google LLC
Inventor： Ting Liu , Gautam Prasad , Phuc Xuan Nguyen , Bohyung Han
Abstract: Systems and methods for a weakly supervised action localization model are provided. Example models according to example aspects of the present disclosure can localize and/or classify actions in untrimmed videos using machine-learned models, such as convolutional neural networks. The example models can predict temporal intervals of human actions given video-level class labels with no requirement of temporal localization information of actions. The example models can recognize actions and identify a sparse set of keyframes associated with actions through adaptive temporal pooling of video frames, wherein the loss function of the model is composed of a classification error and a sparsity of frame selection. Following action recognition with sparse keyframe attention, temporal proposals for action can be extracted using temporal class activation mappings, and final time intervals can be estimated corresponding to target actions.
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公开(公告)号：US20230215169A1
公开(公告)日：2023-07-06
申请号：US18181806
申请日：2023-03-10
Applicant: Google LLC
Inventor： Ting Liu , Gautam Prasad , Phuc Xuan Nguyen , Bohyung Han
IPC: G06V20/40 , G06F18/214 , G06F18/243
CPC classification number: G06V20/40 , G06F18/214 , G06F18/24317 , G06V20/44
Abstract: Systems and methods for a weakly supervised action localization model are provided. Example models according to example aspects of the present disclosure can localize and/or classify actions in untrimmed videos using machine-learned models, such as convolutional neural networks. The example models can predict temporal intervals of human actions given video-level class labels with no requirement of temporal localization information of actions. The example models can recognize actions and identify a sparse set of keyframes associated with actions through adaptive temporal pooling of video frames, wherein the loss function of the model is composed of a classification error and a sparsity of frame selection. Following action recognition with sparse keyframe attention, temporal proposals for action can be extracted using temporal class activation mappings, and final time intervals can be estimated corresponding to target actions.
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公开(公告)号：US11989345B1
公开(公告)日：2024-05-21
申请号：US18548439
申请日：2021-05-28
Applicant: Google LLC
Inventor： Gautam Prasad , Dmitry Lagun , Florian Schroff
CPC classification number: G06F3/013 , G06T7/70 , G06T2207/20081 , G06T2207/20084 , G06T2207/30201
Abstract: A method includes determining a measured eye gaze position of an eye of a user. The method also includes determining a first incremental change in the measured eye gaze position by processing the measured eye gaze position by a long short-term memory (LSTM) model, and determining a first predicted eye gaze position of the eye at a first future time based on the measured eye gaze position and the first incremental change. The method additionally includes determining a second incremental change in the first predicted eye gaze position by processing the first predicted eye gaze position by the LSTM model, and determining a second predicted eye gaze position of the eye at a second future time subsequent to the first future time based on the first predicted eye gaze position and the second incremental change.
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公开(公告)号：US20240143077A1
公开(公告)日：2024-05-02
申请号：US18548439
申请日：2021-05-28
Applicant: Google LLC
Inventor： Gautam Prasad , Dmitry Lagun , Florian Schroff
CPC classification number: G06F3/013 , G06T7/70 , G06T2207/20081 , G06T2207/20084 , G06T2207/30201
Abstract: A method includes determining a measured eye gaze position of an eye of a user. The method also includes determining a first incremental change in the measured eye gaze position by processing the measured eye gaze position by a long short-term memory (LSTM) model, and determining a first predicted eye gaze position of the eye at a first future time based on the measured eye gaze position and the first incremental change. The method additionally includes determining a second incremental change in the first predicted eye gaze position by processing the first predicted eye gaze position by the LSTM model, and determining a second predicted eye gaze position of the eye at a second future time subsequent to the first future time based on the first predicted eye gaze position and the second incremental change.
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公开(公告)号：US20200272823A1
公开(公告)日：2020-08-27
申请号：US16625172
申请日：2018-11-05
Applicant: Google LLC
Inventor： Ting Liu , Gautam Prasad , Phuc Xuan Nguyen , Bohyung Han
Abstract: Systems and methods for a weakly supervised action localization model are provided. Example models according to example aspects of the present disclosure can localize and/or classify actions in untrimmed videos using machine-learned models, such as convolutional neural networks. The example models can predict temporal intervals of human actions given video-level class labels with no requirement of temporal localization information of actions. The example models can recognize actions and identify a sparse set of keyframes associated with actions through adaptive temporal pooling of video frames, wherein the loss function of the model is composed of a classification error and a sparsity of frame selection. Following action recognition with sparse keyframe attention, temporal proposals for action can be extracted using temporal class activation mappings, and final time intervals can be estimated corresponding to target actions.
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