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公开(公告)号：US20210409836A1
公开(公告)日：2021-12-30
申请号：US17470441
申请日：2021-09-09
Applicant: Adobe Inc.
Inventor： Bryan Russell , Ruppesh Nalwaya , Markus Woodson , Joon-Young Lee , Hailin Jin
IPC: H04N21/81 , H04N21/845 , G06N3/08 , G06K9/00
Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for automatic tagging of videos. In particular, in one or more embodiments, the disclosed systems generate a set of tagged feature vectors (e.g., tagged feature vectors based on action-rich digital videos) to utilize to generate tags for an input digital video. For instance, the disclosed systems can extract a set of frames for the input digital video and generate feature vectors from the set of frames. In some embodiments, the disclosed systems generate aggregated feature vectors from the feature vectors. Furthermore, the disclosed systems can utilize the feature vectors (or aggregated feature vectors) to identify similar tagged feature vectors from the set of tagged feature vectors. Additionally, the disclosed systems can generate a set of tags for the input digital videos by aggregating one or more tags corresponding to identified similar tagged feature vectors.
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公开(公告)号：US12112771B2
公开(公告)日：2024-10-08
申请号：US18185137
申请日：2023-03-16
Applicant: Adobe Inc.
Inventor： Simon Jenni , Markus Woodson , Fabian David Caba Heilbron
IPC: G11B27/00 , H04N21/234 , H04N21/2343 , H04N21/24
CPC classification number: G11B27/005 , H04N21/23418 , H04N21/234381 , H04N21/2402
Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that generate a temporally remapped video that satisfies a desired target duration while preserving natural video dynamics. In certain instances, the disclosed systems utilize a playback speed prediction machine-learning model that recognizes and localizes temporally varying changes in video playback speed to re-time a digital video with varying frame-change speeds. For instance, to re-time the digital video, the disclosed systems utilize the playback speed prediction machine-learning model to infer the slowness of individual video frames. Subsequently, in certain embodiments, the disclosed systems determine, from frames of a digital video, a temporal frame sub-sampling that is consistent with the slowness predictions and fit within a target video duration. In certain implementations, the disclosed systems utilize the temporal frame sub-sampling to generate a speed varying digital video that preserves natural video dynamics while fitting the target video duration.
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公开(公告)号：US20230276084A1
公开(公告)日：2023-08-31
申请号：US18185137
申请日：2023-03-16
Applicant: Adobe Inc.
Inventor： Simon Jenni , Markus Woodson , Fabian David Caba Heilbron
IPC: H04N21/2343 , H04N21/234 , H04N21/24
CPC classification number: H04N21/234381 , H04N21/23418 , H04N21/2402
Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that generate a temporally remapped video that satisfies a desired target duration while preserving natural video dynamics. In certain instances, the disclosed systems utilize a playback speed prediction machine-learning model that recognizes and localizes temporally varying changes in video playback speed to re-time a digital video with varying frame-change speeds. For instance, to re-time the digital video, the disclosed systems utilize the playback speed prediction machine-learning model to infer the slowness of individual video frames. Subsequently, in certain embodiments, the disclosed systems determine, from frames of a digital video, a temporal frame sub-sampling that is consistent with the slowness predictions and fit within a target video duration. In certain implementations, the disclosed systems utilize the temporal frame sub-sampling to generate a speed varying digital video that preserves natural video dynamics while fitting the target video duration.
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公开(公告)号：US11610606B1
公开(公告)日：2023-03-21
申请号：US17652586
申请日：2022-02-25
Applicant: Adobe Inc.
Inventor： Simon Jenni , Markus Woodson , Fabian David Caba Heilbron
Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that generate a temporally remapped video that satisfies a desired target duration while preserving natural video dynamics. In certain instances, the disclosed systems utilize a playback speed prediction machine-learning model that recognizes and localizes temporally varying changes in video playback speed to re-time a digital video with varying frame-change speeds. For instance, to re-time the digital video, the disclosed systems utilize the playback speed prediction machine-learning model to infer the slowness of individual video frames. Subsequently, in certain embodiments, the disclosed systems determine, from frames of a digital video, a temporal frame sub-sampling that is consistent with the slowness predictions and fit within a target video duration. In certain implementations, the disclosed systems utilize the temporal frame sub-sampling to generate a speed varying digital video that preserves natural video dynamics while fitting the target video duration.
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Patent Agency Ranking
公开(公告)号：US11146862B2
公开(公告)日：2021-10-12
申请号：US16386031
申请日：2019-04-16
Applicant: Adobe Inc.
Inventor： Bryan Russell , Ruppesh Nalwaya , Markus Woodson , Joon-Young Lee , Hailin Jin
IPC: H04N21/81 , H04N21/845 , G06N3/08 , G06K9/00
Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for automatic tagging of videos. In particular, in one or more embodiments, the disclosed systems generate a set of tagged feature vectors (e.g., tagged feature vectors based on action-rich digital videos) to utilize to generate tags for an input digital video. For instance, the disclosed systems can extract a set of frames for the input digital video and generate feature vectors from the set of frames. In some embodiments, the disclosed systems generate aggregated feature vectors from the feature vectors. Furthermore, the disclosed systems can utilize the feature vectors (or aggregated feature vectors) to identify similar tagged feature vectors from the set of tagged feature vectors. Additionally, the disclosed systems can generate a set of tags for the input digital videos by aggregating one or more tags corresponding to identified similar tagged feature vectors.
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公开(公告)号：US11949964B2
公开(公告)日：2024-04-02
申请号：US17470441
申请日：2021-09-09
Applicant: Adobe Inc.
Inventor： Bryan Russell , Ruppesh Nalwaya , Markus Woodson , Joon-Young Lee , Hailin Jin
IPC: H04N21/81 , G06N3/08 , G06V20/40 , H04N21/845
CPC classification number: H04N21/8133 , G06N3/08 , G06V20/46 , H04N21/8456
Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for automatic tagging of videos. In particular, in one or more embodiments, the disclosed systems generate a set of tagged feature vectors (e.g., tagged feature vectors based on action-rich digital videos) to utilize to generate tags for an input digital video. For instance, the disclosed systems can extract a set of frames for the input digital video and generate feature vectors from the set of frames. In some embodiments, the disclosed systems generate aggregated feature vectors from the feature vectors. Furthermore, the disclosed systems can utilize the feature vectors (or aggregated feature vectors) to identify similar tagged feature vectors from the set of tagged feature vectors. Additionally, the disclosed systems can generate a set of tags for the input digital videos by aggregating one or more tags corresponding to identified similar tagged feature vectors.
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公开(公告)号：US11244204B2
公开(公告)日：2022-02-08
申请号：US16879362
申请日：2020-05-20
Applicant: Adobe Inc.
Inventor： Oliver Wang , Nico Alexander Becherer , Markus Woodson , Federico Perazzi , Nikhil Kalra
Abstract: In implementations of determining video cuts in video clips, a video cut detection system can receive a video clip that includes a sequence of digital video frames that depict one or more scenes. The video cut detection system can determine scene characteristics for the digital video frames. The video cut detection system can determine, from the scene characteristics, a probability of a video cut between two adjacent digital video frames having a boundary between the two adjacent digital video frames that is centered in the sequence of digital video frames. The video cut detection system can then compare the probability of the video cut to a cut threshold to determine whether the video cut exists between the two adjacent digital video frames.
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公开(公告)号：US20210365742A1
公开(公告)日：2021-11-25
申请号：US16879362
申请日：2020-05-20
Applicant: Adobe Inc.
Inventor： Oliver Wang , Nico Alexander Becherer , Markus Woodson , Federico Perazzi , Nikhil Kalra
Abstract: In implementations of determining video cuts in video clips, a video cut detection system can receive a video clip that includes a sequence of digital video frames that depict one or more scenes. The video cut detection system can determine scene characteristics for the digital video frames. The video cut detection system can determine, from the scene characteristics, a probability of a video cut between two adjacent digital video frames having a boundary between the two adjacent digital video frames that is centered in the sequence of digital video frames. The video cut detection system can then compare the probability of the video cut to a cut threshold to determine whether the video cut exists between the two adjacent digital video frames.
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公开(公告)号：US20200336802A1
公开(公告)日：2020-10-22
申请号：US16386031
申请日：2019-04-16
Applicant: Adobe Inc.
Inventor： Bryan Russell , Ruppesh Nalwaya , Markus Woodson , Joon-Young Lee , Hailin Jin
IPC: H04N21/81 , G06K9/00 , G06N3/08 , H04N21/845
Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for automatic tagging of videos. In particular, in one or more embodiments, the disclosed systems generate a set of tagged feature vectors (e.g., tagged feature vectors based on action-rich digital videos) to utilize to generate tags for an input digital video. For instance, the disclosed systems can extract a set of frames for the input digital video and generate feature vectors from the set of frames. In some embodiments, the disclosed systems generate aggregated feature vectors from the feature vectors. Furthermore, the disclosed systems can utilize the feature vectors (or aggregated feature vectors) to identify similar tagged feature vectors from the set of tagged feature vectors. Additionally, the disclosed systems can generate a set of tags for the input digital videos by aggregating one or more tags corresponding to identified similar tagged feature vectors.
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