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Patent Agency Ranking
公开(公告)号：US10679428B1
公开(公告)日：2020-06-09
申请号：US15990318
申请日：2018-05-25
Applicant: Snap Inc.
Inventor： Travis Chen , Samuel Edward Hare , Yuncheng Li , Tony Mathew , Jonathan Solichin , Jianchao Yang , Ning Zhang
Abstract: Systems, devices, media, and methods are presented for object detection and inserting graphical elements into an image stream in response to detecting the object. The systems and methods detect an object of interest in received frames of a video stream. The systems and methods identify a bounding box for the object of interest and estimate a three-dimensional position of the object of interest based on a scale of the object of interest. The systems and methods generate one or more graphical elements having a size based on the scale of the object of interest and a position based on the three-dimensional position estimated for the object of interest. The one or more graphical elements are generated within the video stream to form a modified video stream. The systems and methods cause presentation of the modified video stream including the object of interest and the one or more graphical elements.
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公开(公告)号：US10657676B1
公开(公告)日：2020-05-19
申请号：US16022536
申请日：2018-06-28
Applicant: Snap Inc.
Inventor： Drake Austin Rehfeld , Rahul Bhupendra Sheth , Ning Zhang
Abstract: Disclosed are methods for encoding information in a graphic image. The information may be encoded so as to have a visual appearance that adopts a particular style, so that the encoded information is visually pleasing in the environment in which it is displayed. An encoder and decoder are trained during an integrated training process, where the encoder is tuned to minimize a loss when its encoded images are decoded. Similarly, the decoder is also trained to minimize loss when decoding the encoded images. Both the encoder and decoder may utilize a convolutional neural network in some aspects to analyze data and/or images. Once data is encoded, a style from a sample image is transferred to the encoded data. When decoding, the decoder may largely ignore the style aspects of the encoded data and decode based on a content portion of the data.
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公开(公告)号：US10552968B1
公开(公告)日：2020-02-04
申请号：US15712990
申请日：2017-09-22
Applicant: Snap Inc.
Inventor： Shenlong Wang , Linjie Luo , Ning Zhang , Jia Li
Abstract: Dense feature scale detection can be implemented using multiple convolutional neural networks trained on scale data to more accurately and efficiently match pixels between images. An input image can be used to generate multiple scaled images. The multiple scaled images are input into a feature net, which outputs feature data for the multiple scaled images. An attention net is used to generate an attention map from the input image. The attention map assigns emphasis as a soft distribution to different scales based on texture analysis. The feature data and the attention data can be combined through a multiplication process and then summed to generate dense features for comparison.
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公开(公告)号：US20190279046A1
公开(公告)日：2019-09-12
申请号：US16424404
申请日：2019-05-28
Applicant: Snap Inc.
Inventor： Wei Han , Jianchao Yang , Ning Zhang , Jia Li
Abstract: Systems, devices, media, and methods are presented for identifying and categorically labeling objects within a set of images. The systems and methods receive an image depicting an object of interest, detect at least a portion of the object of interest within the image using a multilayer object model, determine context information, and identify the object of interest included in two or more bounding boxes.
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公开(公告)号：US20180121762A1
公开(公告)日：2018-05-03
申请号：US15340675
申请日：2016-11-01
Applicant: SNAP INC.
Inventor： Wei Han , Jianchao Yang , Ning Zhang , Jia Li
CPC classification number: G06K9/6267 , G06K9/4604 , G06K9/4628 , G06K9/4671 , G06K9/52 , G06K9/6256 , G06K9/627 , G06K9/66 , G06N3/0454 , G06N3/084 , G06T3/40
Abstract: Systems, devices, media, and methods are presented for identifying and categorically labeling objects within a set of images. The systems and methods receive an image depicting an object of interest, detect at least a portion of the object of interest within the image using a multilayer object model, determine context information, and identify the object of interest included in two or more bounding boxes.
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