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公开(公告)号：US11854206B2
公开(公告)日：2023-12-26
申请号：US17735156
申请日：2022-05-03
Applicant: Adobe Inc.
Inventor： Federico Perazzi , Zhe Lin , Ping Hu , Oliver Wang , Fabian David Caba Heilbron
CPC classification number: G06T7/11 , G06F17/15 , G06N3/045 , G06V10/806 , G06V20/46 , G06V20/49 , G06T2207/10016 , G06T2207/20084
Abstract: A Video Semantic Segmentation System (VSSS) is disclosed that performs accurate and fast semantic segmentation of videos using a set of temporally distributed neural networks. The VSSS receives as input a video signal comprising a contiguous sequence of temporally-related video frames. The VSSS extracts features from the video frames in the contiguous sequence and based upon the extracted features, selects, from a set of labels, a label to be associated with each pixel of each video frame in the video signal. In certain embodiments, a set of multiple neural networks are used to extract the features to be used for video segmentation and the extraction of features is distributed among the multiple neural networks in the set. A strong feature representation representing the entirety of the features is produced for each video frame in the sequence of video frames by aggregating the output features extracted by the multiple neural networks.
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Patent Agency Ranking
公开(公告)号：US20220270370A1
公开(公告)日：2022-08-25
申请号：US17735156
申请日：2022-05-03
Applicant: Adobe Inc.
Inventor： Federico Perazzi , Zhe Lin , Ping Hu , Oliver Wang , Fabian David Caba Heilbron
Abstract: A Video Semantic Segmentation System (VSSS) is disclosed that performs accurate and fast semantic segmentation of videos using a set of temporally distributed neural networks. The VSSS receives as input a video signal comprising a contiguous sequence of temporally-related video frames. The VSSS extracts features from the video frames in the contiguous sequence and based upon the extracted features, selects, from a set of labels, a label to be associated with each pixel of each video frame in the video signal. In certain embodiments, a set of multiple neural networks are used to extract the features to be used for video segmentation and the extraction of features is distributed among the multiple neural networks in the set. A strong feature representation representing the entirety of the features is produced for each video frame in the sequence of video frames by aggregating the output features extracted by the multiple neural networks.
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公开(公告)号：US20230325968A1
公开(公告)日：2023-10-12
申请号：US17714356
申请日：2022-04-06
Applicant: Adobe Inc.
Inventor： Simon Niklaus , Ping Hu
CPC classification number: G06T3/4007 , G06T3/0093 , G06T5/50 , G06T2207/20221 , G06T2207/20016
Abstract: Digital synthesis techniques are described to synthesize a digital image at a target time between a first digital image and a second digital image. To begin, an optical flow generation module is employed to generate optical flows. The digital images and optical flows are then received as an input by a motion refinement system. The motion refinement system is configured to generate data describing many-to-many relationships mapped for pixels in the plurality of digital images and reliability scores of the many-to-many relationships. The reliability scores are then used to resolve overlaps of pixels that are mapped to a same location by a synthesis module to generate a synthesized digital image.
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公开(公告)号：US12169909B2
公开(公告)日：2024-12-17
申请号：US17714356
申请日：2022-04-06
Applicant: Adobe Inc.
Inventor： Simon Niklaus , Ping Hu
IPC: G06T3/4007 , G06T3/18 , G06T5/50
Abstract: Digital synthesis techniques are described to synthesize a digital image at a target time between a first digital image and a second digital image. To begin, an optical flow generation module is employed to generate optical flows. The digital images and optical flows are then received as an input by a motion refinement system. The motion refinement system is configured to generate data describing many-to-many relationships mapped for pixels in the plurality of digital images and reliability scores of the many-to-many relationships. The reliability scores are then used to resolve overlaps of pixels that are mapped to a same location by a synthesis module to generate a synthesized digital image.
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公开(公告)号：US11354906B2
公开(公告)日：2022-06-07
申请号：US16846544
申请日：2020-04-13
Applicant: Adobe Inc.
Inventor： Federico Perazzi , Zhe Lin , Ping Hu , Oliver Wang , Fabian David Caba Heilbron
Abstract: A Video Semantic Segmentation System (VSSS) is disclosed that performs accurate and fast semantic segmentation of videos using a set of temporally distributed neural networks. The VSSS receives as input a video signal comprising a contiguous sequence of temporally-related video frames. The VSSS extracts features from the video frames in the contiguous sequence and based upon the extracted features, selects, from a set of labels, a label to be associated with each pixel of each video frame in the video signal. In certain embodiments, a set of multiple neural networks are used to extract the features to be used for video segmentation and the extraction of features is distributed among the multiple neural networks in the set. A strong feature representation representing the entirety of the features is produced for each video frame in the sequence of video frames by aggregating the output features extracted by the multiple neural networks.
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公开(公告)号：US20210319232A1
公开(公告)日：2021-10-14
申请号：US16846544
申请日：2020-04-13
Applicant: Adobe Inc
Inventor： Federico Perazzi , Zhe Lin , Ping Hu , Oliver Wang , Fabian David Caba Heilbron
Abstract: A Video Semantic Segmentation System (VSSS) is disclosed that performs accurate and fast semantic segmentation of videos using a set of temporally distributed neural networks. The VSSS receives as input a video signal comprising a contiguous sequence of temporally-related video frames. The VSSS extracts features from the video frames in the contiguous sequence and based upon the extracted features, selects, from a set of labels, a label to be associated with each pixel of each video frame in the video signal. In certain embodiments, a set of multiple neural networks are used to extract the features to be used for video segmentation and the extraction of features is distributed among the multiple neural networks in the set. A strong feature representation representing the entirety of the features is produced for each video frame in the sequence of video frames by aggregating the output features extracted by the multiple neural networks.
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