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公开(公告)号：US20190156106A1
公开(公告)日：2019-05-23
申请号：US15818124
申请日：2017-11-20
Applicant: Google LLC
Inventor： Gerhard Florian Schroff , Dmitry Kalenichenko , Keren Ye
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an object embedding system. In one aspect, a method comprises providing selected images as input to the object embedding system and generating corresponding embeddings, wherein the object embedding system comprises a thumbnailing neural network and an embedding neural network. The method further comprises backpropagating gradients based on a loss function to reduce the distance between embeddings for same instances of objects, and to increase the distance between embeddings for different instances of objects.
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公开(公告)号：US11126820B2
公开(公告)日：2021-09-21
申请号：US16842920
申请日：2020-04-08
Applicant: Google LLC
Inventor： Gerhard Florian Schroff , Dmitry Kalenichenko , Keren Ye
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an object embedding system. In one aspect, a method comprises providing selected images as input to the object embedding system and generating corresponding embeddings, wherein the object embedding system comprises a thumbnailing neural network and an embedding neural network. The method further comprises backpropagating gradients based on a loss function to reduce the distance between embeddings for same instances of objects, and to increase the distance between embeddings for different instances of objects.
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公开(公告)号：US20190370648A1
公开(公告)日：2019-12-05
申请号：US16425900
申请日：2019-05-29
Applicant: Google LLC
Inventor： Barret Zoph , Jonathon Shlens , Yukun Zhu , Maxwell Donald Emmet Collins , Liang-Chieh Chen , Gerhard Florian Schroff , Hartwig Adam , Georgios Papandreou
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes obtaining training data for a dense image prediction task; and determining an architecture for a neural network configured to perform the dense image prediction task, comprising: searching a space of candidate architectures to identify one or more best performing architectures using the training data, wherein each candidate architecture in the space of candidate architectures comprises (i) the same first neural network backbone that is configured to receive an input image and to process the input image to generate a plurality of feature maps and (ii) a different dense prediction cell configured to process the plurality of feature maps and to generate an output for the dense image prediction task; and determining the architecture for the neural network based on the best performing candidate architectures.
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公开(公告)号：US10452954B2
公开(公告)日：2019-10-22
申请号：US15704746
申请日：2017-09-14
Applicant: Google LLC
Inventor： Gerhard Florian Schroff , Wenze Hu
IPC: G06K9/62 , G06K9/00 , G06F16/583
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for object detection and representation in images. In one aspect, a method includes detecting occurrences of objects of a particular type in images captured within a first duration of time, and iteratively training an image embedding function to produce as output representations of features of the input images depicting occurrences of objects of the particular type, where similar representations of features are generated for images that depict the same instance of an object of a particular type captured within a specified duration of time, and dissimilar representations of features are generated for images that depict different instances of objects of the particular type.
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公开(公告)号：US11074504B2
公开(公告)日：2021-07-27
申请号：US16611604
申请日：2018-11-14
Applicant: Google LLC
Inventor： Liang-Chieh Chen , Alexander Hermans , Georgios Papandreou , Gerhard Florian Schroff , Peng Wang , Hartwig Adam
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for instance segmentation. In one aspect, a system generates: (i) data identifying one or more regions of the image, wherein an object is depicted in each region, (ii) for each region, a predicted type of object that is depicted in the region, and (iii) feature channels comprising a plurality of semantic channels and one or more direction channels. The system generates a region descriptor for each of the one or more regions, and provides the region descriptor for each of the one or more regions to a segmentation neural network that processes a region descriptor for a region to generate a predicted segmentation of the predicted type of object depicted in the region.
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Patent Agency Ranking
公开(公告)号：US20210081796A1
公开(公告)日：2021-03-18
申请号：US17107745
申请日：2020-11-30
Applicant: Google LLC
Inventor： Barret Zoph , Jonathon Shlens , Yukun Zhu , Maxwell Donald Collins , Liang-Chieh Chen , Gerhard Florian Schroff , Hartwig Adam , Georgios Papandreou
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes obtaining training data for a dense image prediction task; and determining an architecture for a neural network configured to perform the dense image prediction task, comprising: searching a space of candidate architectures to identify one or more best performing architectures using the training data, wherein each candidate architecture in the space of candidate architectures comprises (i) the same first neural network backbone that is configured to receive an input image and to process the input image to generate a plurality of feature maps and (ii) a different dense prediction cell configured to process the plurality of feature maps and to generate an output for the dense image prediction task; and determining the architecture for the neural network based on the best performing candidate architectures.
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公开(公告)号：US10853726B2
公开(公告)日：2020-12-01
申请号：US16425900
申请日：2019-05-29
Applicant: Google LLC
Inventor： Barret Zoph , Jonathon Shlens , Yukun Zhu , Maxwell Donald Emmet Collins , Liang-Chieh Chen , Gerhard Florian Schroff , Hartwig Adam , Georgios Papandreou
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes obtaining training data for a dense image prediction task; and determining an architecture for a neural network configured to perform the dense image prediction task, comprising: searching a space of candidate architectures to identify one or more best performing architectures using the training data, wherein each candidate architecture in the space of candidate architectures comprises (i) the same first neural network backbone that is configured to receive an input image and to process the input image to generate a plurality of feature maps and (ii) a different dense prediction cell configured to process the plurality of feature maps and to generate an output for the dense image prediction task; and determining the architecture for the neural network based on the best performing candidate architectures.
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公开(公告)号：US20200242333A1
公开(公告)日：2020-07-30
申请号：US16842920
申请日：2020-04-08
Applicant: Google LLC
Inventor： Gerhard Florian Schroff , Dmitry Kalenichenko , Keren Ye
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an object embedding system. In one aspect, a method comprises providing selected images as input to the object embedding system and generating corresponding embeddings, wherein the object embedding system comprises a thumbnailing neural network and an embedding neural network. The method further comprises backpropagating gradients based on a loss function to reduce the distance between embeddings for same instances of objects, and to increase the distance between embeddings for different instances of objects.
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公开(公告)号：US20200175375A1
公开(公告)日：2020-06-04
申请号：US16611604
申请日：2018-11-14
Applicant: Google LLC
Inventor： Liang-Chieh Chen , Alexander Hermans , Georgios Papandreou , Gerhard Florian Schroff , Peng Wang , Hartwig Adam
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for instance segmentation. In one aspect, a system generates: (i) data identifying one or more regions of the image, wherein an object is depicted in each region, (ii) for each region, a predicted type of object that is depicted in the region, and (iii) feature channels comprising a plurality of semantic channels and one or more direction channels. The system generates a region descriptor for each of the one or more regions, and provides the region descriptor for each of the one or more regions to a segmentation neural network that processes a region descriptor for a region to generate a predicted segmentation of the predicted type of object depicted in the region.
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公开(公告)号：US10657359B2
公开(公告)日：2020-05-19
申请号：US15818124
申请日：2017-11-20
Applicant: Google LLC
Inventor： Gerhard Florian Schroff , Dmitry Kalenichenko , Keren Ye
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an object embedding system. In one aspect, a method comprises providing selected images as input to the object embedding system and generating corresponding embeddings, wherein the object embedding system comprises a thumbnailing neural network and an embedding neural network. The method further comprises backpropagating gradients based on a loss function to reduce the distance between embeddings for same instances of objects, and to increase the distance between embeddings for different instances of objects.
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