-
公开(公告)号：US20200320353A1
公开(公告)日：2020-10-08
申请号：US16946346
申请日：2020-06-17
Applicant: Snap Inc.
Inventor： Linjie Yang , Kevin Dechau Tang , Jianchao Yang , Jia Li
Abstract: A dense captioning system and method is provided for analyzing an image to generate proposed bounding regions for a plurality of visual concepts within the image, generating a region feature for each proposed bounding region to generate a plurality of region features of the image, and determining a context feature for the image using a proposed bounding region that is a largest in size of the proposed bounding regions. For each region feature of the plurality of region features of the image, the dense captioning system and method further provides for analyzing the region feature to determine for the region feature a detection score that indicates a likelihood that the region feature comprises an actual object, and generating a caption for a visual concept in the image using the region feature and the context feature when a detection score is above a specified threshold value.
-
公开(公告)号：US10474900B2
公开(公告)日：2019-11-12
申请号：US15706096
申请日：2017-09-15
Applicant: Snap Inc.
Inventor： Samuel Edward Hare , Fedir Poliakov , Guohui Wang , Xuehan Xiong , Jianchao Yang , Linjie Yang , Shah Tanmay Anilkumar
Abstract: A mobile device can generate real-time complex visual image effects using asynchronous processing pipeline. A first pipeline applies a complex image process, such as a neural network, to keyframes of a live image sequence. A second pipeline generates flow maps that describe feature transformations in the image sequence. The flow maps can be used to process non-keyframes on the fly. The processed keyframes and non-keyframes can be used to display a complex visual effect on the mobile device in real-time or near real-time.
-
公开(公告)号：US20240372963A1
公开(公告)日：2024-11-07
申请号：US18772971
申请日：2024-07-15
Applicant: Snap inc.
Inventor： Lidiia Bogdanovych , William Brendel , Samuel Edward Hare , Fedir Paliakov , Guohui Wang , Xuehan Xiong , Jianchao Yang , Linjie Yang
IPC: H04N7/14 , G06F18/214 , G06F18/24 , G06N3/04 , G06N3/08 , G06T7/11 , G06T7/194 , G06V10/82 , G06V30/19 , G06V30/242 , H04N5/445 , H04N5/76
Abstract: A machine learning system can generate an image mask (e.g., a pixel mask) comprising pixel assignments for pixels. The pixels can be assigned to classes, including, for example, face, clothes, body skin, or hair. The machine learning system can be implemented using a convolutional neural network that is configured to execute efficiently on computing devices having limited resources, such as mobile phones. The pixel mask can be used to more accurately display video effects interacting with a user or subject depicted in the image.
-
公开(公告)号：US11989938B2
公开(公告)日：2024-05-21
申请号：US18312479
申请日：2023-05-04
Applicant: Snap Inc.
Inventor： Samuel Edward Hare , Fedir Poliakov , Guohui Wang , Xuehan Xiong , Jianchao Yang , Linjie Yang , Shah Tanmay Anilkumar
CPC classification number: G06V20/40 , G06T1/20 , G06T7/248 , G06V20/46 , G06T2200/28 , G06T2207/10016 , G06T2207/20081
Abstract: A mobile device can generate real-time complex visual image effects using asynchronous processing pipeline. A first pipeline applies a complex image process, such as a neural network, to keyframes of a live image sequence. A second pipeline generates flow maps that describe feature transformations in the image sequence. The flow maps can be used to process non-keyframes on the fly. The processed keyframes and non-keyframes can be used to display a complex visual effect on the mobile device in real-time or near real-time.
-
Patent Agency Ranking
公开(公告)号：US11847528B2
公开(公告)日：2023-12-19
申请号：US18090577
申请日：2022-12-29
Applicant: Snap Inc.
Inventor： Linjie Yang , Jianchao Yang , Xuehan Xiong , Yanran Wang
Abstract: A modulated segmentation system can use a modulator network to emphasize spatial prior data of an object to track the object across multiple images. The modulated segmentation system can use a segmentation network that receives spatial prior data as intermediate data that improves segmentation accuracy. The segmentation network can further receive visual guide information from a visual guide network to increase tracking accuracy via segmentation.
-
公开(公告)号：US11676381B2
公开(公告)日：2023-06-13
申请号：US17248393
申请日：2021-01-22
Applicant: Snap Inc.
Inventor： Samuel Edward Hare , Fedir Poliakov , Guohui Wang , Xuehan Xiong , Jianchao Yang , Linjie Yang , Shah Tanmay Anilkumar
CPC classification number: G06V20/40 , G06T1/20 , G06T7/248 , G06V20/46 , G06T2200/28 , G06T2207/10016 , G06T2207/20081
Abstract: A mobile device can generate real-time complex visual image effects using asynchronous processing pipeline. A first pipeline applies a complex image process, such as a neural network, to keyframes of a live image sequence. A second pipeline generates flow maps that describe feature transformations in the image sequence. The flow maps can be used to process non-keyframes on the fly. The processed keyframes and non-keyframes can be used to display a complex visual effect on the mobile device in real-time or near real-time.
-
公开(公告)号：US11182603B1
公开(公告)日：2021-11-23
申请号：US16450376
申请日：2019-06-24
Applicant: Snap Inc.
Inventor： Yuncheng Li , Linjie Yang , Ning Zhang , Zhengyuan Yang
Abstract: Segmentation of an image into individual body parts is performed based on a trained model. The model is trained with a plurality of training images, each training image representing a corresponding training figure. The model is also trained with a corresponding plurality of segmentations of the training figures. Each segmentation is generated by positioning body parts between defined positions of joints of the represented figure. The body parts are represented by body part templates obtained from a template library, with the templates defining characteristics of body parts represented by the templates.
-
公开(公告)号：US10776663B1
公开(公告)日：2020-09-15
申请号：US16521956
申请日：2019-07-25
Applicant: Snap Inc.
Inventor： Lidiia Bogdanovych , William Brendel , Samuel Edward Hare , Fedir Poliakov , Guohui Wang , Xuehan Xiong , Jianchao Yang , Linjie Yang
IPC: G06K9/62 , G06T7/11 , G06T7/194 , G06N3/08 , G06N3/04 , G06K9/68 , G06K9/74 , H04N5/76 , H04N5/445 , H04N7/14
Abstract: A machine learning system can generate an image mask (e.g., a pixel mask) comprising pixel assignments for pixels. The pixels can be assigned to classes, including, for example, face, clothes, body skin, or hair. The machine learning system can be implemented using a convolutional neural network that is configured to execute efficiently on computing devices having limited resources, such as mobile phones. The pixel mask can be used to more accurately display video effects interacting with a user or subject depicted in the image.
-
公开(公告)号：US10198671B1
公开(公告)日：2019-02-05
申请号：US15348501
申请日：2016-11-10
Applicant: SNAP INC.
Inventor： Linjie Yang , Kevin Dechau Tang , Jianchao Yang , Jia Li
Abstract: A dense captioning system and method is provided for processing an image to produce a feature map of the image, analyzing the feature map to generate proposed bounding boxes for a plurality of visual concepts within the image, analyzing the feature map to determine a plurality of region features of the image, and analyzing the feature map to determine a context feature for the image. For each region feature of the plurality of region features of the image, the dense captioning system further provides for analyzing the region feature to determine a detection score for the region feature, calculating a caption for a bounding box for a visual concept in the image using the region feature and the context feature, and localizing the visual concept by adjusting the bounding box around the visual concept based on the caption to generate an adjusted bounding box for the visual concept.
-
-
-
-
-
-
-
-
Search Results
29
records
(took 0.021 seconds)
