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公开(公告)号：US20250022252A1
公开(公告)日：2025-01-16
申请号：US18899571
申请日：2024-09-27
Applicant: Adobe Inc.
Inventor： Khoi Pham , Kushal Kafle , Zhe Lin , Zhihong Ding , Scott Cohen , Quan Tran
IPC: G06V10/75 , G06F18/214 , G06F18/25 , G06N3/08
Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that extract multiple attributes from an object portrayed in a digital image utilizing a multi-attribute contrastive classification neural network. For example, the disclosed systems utilize a multi-attribute contrastive classification neural network that includes an embedding neural network, a localizer neural network, a multi-attention neural network, and a classifier neural network. In some cases, the disclosed systems train the multi-attribute contrastive classification neural network utilizing a multi-attribute, supervised-contrastive loss. In some embodiments, the disclosed systems generate negative attribute training labels for labeled digital images utilizing positive attribute labels that correspond to the labeled digital images.
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公开(公告)号：US12154196B2
公开(公告)日：2024-11-26
申请号：US17810392
申请日：2022-07-01
Applicant: Adobe Inc.
Inventor： Zhifei Zhang , Zhe Lin , Scott Cohen , Darshan Prasad , Zhihong Ding
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for transferring global style features between digital images utilizing one or more machine learning models or neural networks. In particular, in one or more embodiments, the disclosed systems receive a request to transfer a global style from a source digital image to a target digital image, identify at least one target object within the target digital image, and transfer the global style from the source digital image to the target digital image while maintaining an object style of the at least one target object.
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3.发明公开公开(公告)号：US20240004924A1
公开(公告)日：2024-01-04
申请号：US17809781
申请日：2022-06-29
Applicant: Adobe Inc.
Inventor： Zhifei Zhang , Zhe Lin , Zhihong Ding , Scott Cohen , Darshan Prasad
IPC: G06F16/538 , G06F16/532 , G06T7/11 , G06T5/50 , G06F16/583
CPC classification number: G06F16/538 , G06F16/532 , G06F16/5838 , G06T5/50 , G06T7/11
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that implements related image search and image modification processes using various search engines and a consolidated graphical user interface. For instance, in one or more embodiments, the disclosed systems receive an input digital image and search input and further modify the input digital image using the image search results retrieved in response to the search input. In some cases, the search input includes a multi-modal search input having multiple queries (e.g., an image query and a text query), and the disclosed systems retrieve the image search results utilizing a weighted combination of the queries. In some implementations, the disclosed systems generate an input embedding for the search input (e.g., the multi-modal search input) and retrieve the image search results using the input embedding.
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公开(公告)号：US20220058777A1
公开(公告)日：2022-02-24
申请号：US16997364
申请日：2020-08-19
Applicant: Adobe Inc.
Inventor： Scott David Cohen , Zhihong Ding , Zhe Lin , Mingyang Ling , Luis Angel Figueroa
Abstract: Systems, methods, and software are described herein for removing people distractors from images. A distractor mitigation solution implemented in one or more computing devices detects people in an image and identifies salient regions in the image. The solution then determines a saliency cue for each person and classifies each person as wanted or as an unwanted distractor based at least on the saliency cue. An unwanted person is then removed from the image or otherwise reduced from the perspective of being an unwanted distraction.
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公开(公告)号：US11152032B2
公开(公告)日：2021-10-19
申请号：US16395041
申请日：2019-04-25
Applicant: Adobe Inc.
Inventor： Zhihong Ding , Zhe Lin , Xiaohui Shen , Michael Kaplan , Jonathan Brandt
Abstract: The present disclosure is directed toward systems and methods for tracking objects in videos. For example, one or more embodiments described herein utilize various tracking methods in combination with an image search index made up of still video frames indexed from a video. One or more embodiments described herein utilize a backward and forward tracking method that is anchored by one or more key frames in order to accurately track an object through the frames of a video, even when the video is long and may include challenging conditions.
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Patent Agency Ranking
公开(公告)号：US10319412B2
公开(公告)日：2019-06-11
申请号：US15353186
申请日：2016-11-16
Applicant: Adobe Inc.
Inventor： Zhihong Ding , Zhe Lin , Xiaohui Shen , Michael Kaplan , Jonathan Brandt
Abstract: The present disclosure is directed toward systems and methods for tracking objects in videos. For example, one or more embodiments described herein utilize various tracking methods in combination with an image search index made up of still video frames indexed from a video. One or more embodiments described herein utilize a backward and forward tracking method that is anchored by one or more key frames in order to accurately track an object through the frames of a video, even when the video is long and may include challenging conditions.
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公开(公告)号：US20250069297A1
公开(公告)日：2025-02-27
申请号：US18948839
申请日：2024-11-15
Applicant: Adobe Inc.
Inventor： Zhifei Zhang , Zhe Lin , Scott Cohen , Darshan Prasad , Zhihong Ding
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for transferring global style features between digital images utilizing one or more machine learning models or neural networks. In particular, in one or more embodiments, the disclosed systems receive a request to transfer a global style from a source digital image to a target digital image, identify at least one target object within the target digital image, and transfer the global style from the source digital image to the target digital image while maintaining an object style of the at least one target object.
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公开(公告)号：US20240169628A1
公开(公告)日：2024-05-23
申请号：US18460150
申请日：2023-09-01
Applicant: Adobe Inc.
Inventor： Soo Ye Kim , Zhe Lin , Scott Cohen , Jianming Zhang , Luis Figueroa , Zhihong Ding
IPC: G06T11/60 , G06F3/0481 , G06F3/04845 , G06F3/0486 , G06T5/00 , G06T11/00
CPC classification number: G06T11/60 , G06F3/0481 , G06F3/04845 , G06F3/0486 , G06T5/002 , G06T5/005 , G06T11/001 , G06T2200/24 , G06T2207/20092 , G06T2207/20212
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that provides a graphical user interface experience to move objects and generate new shadows within a digital image scene. For instance, in one or more embodiments, the disclosed systems receive a digital image depicting a scene. The disclosed systems receive a selection to position an object in a first location within the scene. Further, the disclosed systems composite an image by placing the object at the first location within the scene of the digital image. Moreover, the disclosed systems generate a modified digital image having a shadow of the object where the shadow is consistent with the scene and provides the modified digital image to the client device.
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9.发明公开公开(公告)号：US20240169624A1
公开(公告)日：2024-05-23
申请号：US18058538
申请日：2022-11-23
Applicant: Adobe Inc.
Inventor： Jonathan Brandt , Scott Cohen , Zhe Lin , Zhihong Ding , Darshan Prasad , Matthew Joss , Celso Gomes , Jianming Zhang , Olena Soroka , Klaas Stoeckmann , Michael Zimmermann , Thomas Muehrke
IPC: G06T11/60 , G06F3/04842 , G06F3/04845 , G06T11/40
CPC classification number: G06T11/60 , G06F3/04842 , G06F3/04845 , G06T11/40
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems generate utilizing a segmentation neural network, an object mask for each object of a plurality of objects of a digital image. The disclosed systems detect a first user interaction with an object in the digital image displayed via a graphical user interface. The disclosed systems surface, via the graphical user interface, the object mask for the object in response to the first user interaction. The disclosed systems perform an object-aware modification of the digital image in response to a second user interaction with the object mask for the object.
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公开(公告)号：US11886494B2
公开(公告)日：2024-01-30
申请号：US17929206
申请日：2022-09-01
Applicant: Adobe Inc.
Inventor： Walter Wei Tuh Chang , Khoi Pham , Scott Cohen , Zhe Lin , Zhihong Ding
IPC: G06F16/583 , G06F16/532 , G06F16/33 , G06T11/60 , G06F40/279 , G06F40/247 , G06N20/00 , G06F16/242 , G06F16/28 , G06F16/538 , G06F40/30 , G06F18/2431 , G06V10/82
CPC classification number: G06F16/5854 , G06F16/243 , G06F16/288 , G06F16/3344 , G06F16/532 , G06F16/538 , G06F18/2431 , G06F40/247 , G06F40/279 , G06F40/30 , G06N20/00 , G06T11/60 , G06V10/82
Abstract: The present disclosure relates to an object selection system that automatically detects and selects objects in a digital image based on natural language-based inputs. For instance, the object selection system can utilize natural language processing tools to detect objects and their corresponding relationships within natural language object selection queries. For example, the object selection system can determine alternative object terms for unrecognized objects in a natural language object selection query. As another example, the object selection system can determine multiple types of relationships between objects in a natural language object selection query and utilize different object relationship models to select the requested query object.
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