Patents by Inventor Lubomir Dimitrov Bourdev
Lubomir Dimitrov Bourdev has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11380119Abstract: In one embodiment, a method includes locating a plurality of part patches from an image, wherein each part patch comprises at least a portion of the image corresponding to a recognized human body portion or pose, and wherein each part patch is associated with a respective detection score larger than a threshold score, wherein the detection score is determined based on a comparison between the part patch with multiple training patches, generating a plurality of sets of feature data by processing each of the plurality of part patches with a plurality of convolutional neural networks, respectively, and determining whether a human attribute exists in the image based on the plurality of sets of feature data.Type: GrantFiled: June 17, 2019Date of Patent: July 5, 2022Assignee: Meta Platforms, Inc.Inventor: Lubomir Dimitrov Bourdev
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Publication number: 20210279817Abstract: Systems, methods, and non-transitory computer-readable media can receive a compressed convolutional neural network (CNN). A media content item to be processed can be acquired. The compressed CNN to can be utilized to apply a media processing technique to the media content item to produce information about the media content item. It can be determined, based on at least some of the information about the media content item, whether to transmit at least a portion of the media content item to one or more remote servers for additional media processing.Type: ApplicationFiled: February 8, 2021Publication date: September 9, 2021Inventors: Yunchao Gong, Liu Liu, Lubomir Dimitrov Bourdev, Robert D. Fergus, Ming Yang
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Publication number: 20190303660Abstract: In one embodiment, a method includes locating a plurality of part patches from an image, wherein each part patch comprises at least a portion of the image corresponding to a recognized human body portion or pose, and wherein each part patch is associated with a respective detection score larger than a threshold score, wherein the detection score is determined based on a comparison between the part patch with multiple training patches, generating a plurality of sets of feature data by processing each of the plurality of part patches with a plurality of convolutional neural networks, respectively, and determining whether a human attribute exists in the image based on the plurality of sets of feature data.Type: ApplicationFiled: June 17, 2019Publication date: October 3, 2019Inventor: Lubomir Dimitrov Bourdev
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Patent number: 10402632Abstract: Technology is disclosed for inferring human attributes from images of people. The attributes can include, for example, gender, age, hair, and/or clothing. The technology uses part-based models, e.g., Poselets, to locate multiple normalized part patches from an image. The normalized part patches are provided into trained convolutional neural networks to generate feature data. Each convolution neural network applies multiple stages of convolution operations to one part patch to generate a set of fully connected feature data. The feature data for all part patches are concatenated and then provided into multiple trained classifiers (e.g., linear support vector machines) to predict attributes of the image.Type: GrantFiled: July 19, 2016Date of Patent: September 3, 2019Assignee: Facebook, Inc.Inventor: Lubomir Dimitrov Bourdev
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Publication number: 20170132510Abstract: In one embodiment, a method may include receiving a first content item. A first embedding of the first content item may be determined and may corresponds to a first point in an embedding space. The embedding space may include a plurality of second points corresponding to a plurality of second embeddings of second content items. The embeddings are determined using a deep-learning model. The points are located in one or more clusters in the embedding space, which are each associated with a class of content items. Locations of points within clusters may be based on one or more attributes of the respective corresponding content items. Second content items that are similar to the first content item may be identified based on the locations of the first point and the second points and on particular clusters that the second points corresponding to the identified second content items are located in.Type: ApplicationFiled: December 28, 2015Publication date: May 11, 2017Inventors: Balmanohar Paluri, Oren Rippel, Piotr Dollar, Lubomir Dimitrov Bourdev
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Publication number: 20170091576Abstract: The techniques introduced here include a system and method for transcoding multimedia content based on the results of content analysis. The determination of specific transcoding parameters, used for transcoding multimedia content, can be performed by utilizing the results of content analysis of the multimedia content. One of the results of the content analysis is the determination of image type of any images included in the multimedia content. The content analysis uses one or more of several techniques, including analyzing content metadata, examining colors of contiguous pixels in the content, using histogram analysis, using compression distortion analysis, analyzing image edges, or examining user provided inputs. Transcoding the multimedia content can include adapting the content to the constraints in delivery and display, processing and storage of user computing devices.Type: ApplicationFiled: December 12, 2016Publication date: March 30, 2017Inventors: Apostolos Lerios, Dirk John Stoop, Ryan Mack, Lubomir Dimitrov Bourdev, Balmanohar Paluri
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Patent number: 9558422Abstract: The techniques introduced here include a system and method for transcoding multimedia content based on the results of content analysis. The determination of specific transcoding parameters, used for transcoding multimedia content, can be performed by utilizing the results of content analysis of the multimedia content. One of the results of the content analysis is the determination of image type of any images included in the multimedia content. The content analysis uses one or more of several techniques, including analyzing content metadata, examining colors of contiguous pixels in the content, using histogram analysis, using compression distortion analysis, analyzing image edges, or examining user provided inputs. Transcoding the multimedia content can include adapting the content to the constraints in delivery and display, processing and storage of user computing devices.Type: GrantFiled: January 22, 2016Date of Patent: January 31, 2017Assignee: Facebook, Inc.Inventors: Apostolos Lerios, Dirk John Stoop, Ryan Mack, Lubomir Dimitrov Bourdev, Balmanohar Paluri
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Publication number: 20160328606Abstract: Technology is disclosed for inferring human attributes from images of people. The attributes can include, for example, gender, age, hair, and/or clothing. The technology uses part-based models, e.g., Poselets, to locate multiple normalized part patches from an image. The normalized part patches are provided into trained convolutional neural networks to generate feature data. Each convolution neural network applies multiple stages of convolution operations to one part patch to generate a set of fully connected feature data. The feature data for all part patches are concatenated and then provided into multiple trained classifiers (e.g., linear support vector machines) to predict attributes of the image.Type: ApplicationFiled: July 19, 2016Publication date: November 10, 2016Inventor: Lubomir Dimitrov Bourdev
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Patent number: 9400925Abstract: Technology is disclosed for inferring human attributes from images of people. The attributes can include, for example, gender, age, hair, and/or clothing. The technology uses part-based models, e.g., Poselets, to locate multiple normalized part patches from an image. The normalized part patches are provided into trained convolutional neural networks to generate feature data. Each convolution neural network applies multiple stages of convolution operations to one part patch to generate a set of fully connected feature data. The feature data for all part patches are concatenated and then provided into multiple trained classifiers (e.g., linear support vector machines) to predict attributes of the image.Type: GrantFiled: February 7, 2014Date of Patent: July 26, 2016Assignee: FACEBOOK, INC.Inventor: Lubomir Dimitrov Bourdev
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Publication number: 20160140415Abstract: The techniques introduced here include a system and method for transcoding multimedia content based on the results of content analysis. The determination of specific transcoding parameters, used for transcoding multimedia content, can be performed by utilizing the results of content analysis of the multimedia content. One of the results of the content analysis is the determination of image type of any images included in the multimedia content. The content analysis uses one or more of several techniques, including analyzing content metadata, examining colors of contiguous pixels in the content, using histogram analysis, using compression distortion analysis, analyzing image edges, or examining user provided inputs. Transcoding the multimedia content can include adapting the content to the constraints in delivery and display, processing and storage of user computing devices.Type: ApplicationFiled: January 22, 2016Publication date: May 19, 2016Inventors: Apostolos Lerios, Dirk John Stoop, Ryan Mack, Lubomir Dimitrov Bourdev, Balmanohar Paluri
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Patent number: 9280723Abstract: The techniques introduced here include a system and method for transcoding multimedia content based on the results of content analysis. The determination of specific transcoding parameters, used for transcoding multimedia content, can be performed by utilizing the results of content analysis of the multimedia content. One of the results of the content analysis is the determination of image type of any images included in the multimedia content. The content analysis uses one or more of several techniques, including analyzing content metadata, examining colors of contiguous pixels in the content, using histogram analysis, using compression distortion analysis, analyzing image edges, or examining user provided inputs. Transcoding the multimedia content can include adapting the content to the constraints in delivery and display, processing and storage of user computing devices.Type: GrantFiled: October 28, 2014Date of Patent: March 8, 2016Assignee: FACEBOOK, INC.Inventors: Apostolos Lerios, Dirk Stoop, Ryan Mack, Lubomir Dimitrov Bourdev, Balmanohar Paluri
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Publication number: 20150139485Abstract: Technology is disclosed for inferring human attributes from images of people. The attributes can include, for example, gender, age, hair, and/or clothing. The technology uses part-based models, e.g., Poselets, to locate multiple normalized part patches from an image. The normalized part patches are provided into trained convolutional neural networks to generate feature data. Each convolution neural network applies multiple stages of convolution operations to one part patch to generate a set of fully connected feature data. The feature data for all part patches are concatenated and then provided into multiple trained classifiers (e.g., linear support vector machines) to predict attributes of the image.Type: ApplicationFiled: February 7, 2014Publication date: May 21, 2015Inventor: Lubomir Dimitrov Bourdev
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Publication number: 20150110394Abstract: The techniques introduced here include a system and method for transcoding multimedia content based on the results of content analysis. The determination of specific transcoding parameters, used for transcoding multimedia content, can be performed by utilizing the results of content analysis of the multimedia content. One of the results of the content analysis is the determination of image type of any images included in the multimedia content. The content analysis uses one or more of several techniques, including analyzing content metadata, examining colors of contiguous pixels in the content, using histogram analysis, using compression distortion analysis, analyzing image edges, or examining user provided inputs. Transcoding the multimedia content can include adapting the content to the constraints in delivery and display, processing and storage of user computing devices.Type: ApplicationFiled: October 28, 2014Publication date: April 23, 2015Inventors: Apostolos Lerios, Dirk Stoop, Ryan Mack, Lubomir Dimitrov Bourdev, Balmanohar Paluri