公开(公告)号：US12008464B2

公开(公告)日：2024-06-11

申请号：US15815635

申请日：2017-11-16

Applicant: ADOBE INC.

Inventor： Haoxiang Li ,  Zhe Lin ,  Jonathan Brandt ,  Xiaohui Shen

IPC: G06N3/08 ,  G06F3/04812 ,  G06F18/2413 ,  G06N3/045 ,  G06T15/04 ,  G06T15/20 ,  G06V10/44 ,  G06V10/764 ,  G06V10/82 ,  G06V40/16

CPC classification number: G06N3/08 ,  G06F3/04812 ,  G06F18/24143 ,  G06N3/045 ,  G06T15/04 ,  G06T15/205 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  G06V40/165 ,  G06V40/171

Abstract: Approaches are described for determining facial landmarks in images. An input image is provided to at least one trained neural network that determines a face region (e.g., bounding box of a face) of the input image and initial facial landmark locations corresponding to the face region. The initial facial landmark locations are provided to a 3D face mapper that maps the initial facial landmark locations to a 3D face model. A set of facial landmark locations are determined from the 3D face model. The set of facial landmark locations are provided to a landmark location adjuster that adjusts positions of the set of facial landmark locations based on the input image. The input image is presented on a user device using the adjusted set of facial landmark locations.

公开(公告)号：US20240135514A1

公开(公告)日：2024-04-25

申请号：US18460365

申请日：2023-09-01

Applicant: Adobe Inc.

Inventor： Daniil Pakhomov ,  Qing Liu ,  Zhihong Ding ,  Scott Cohen ,  Zhe Lin ,  Jianming Zhang ,  Zhifei Zhang ,  Ohiremen Dibua ,  Mariette Souppe ,  Krishna Kumar Singh ,  Jonathan Brandt

IPC: G06T5/00 ,  G06F3/04845 ,  G06T7/11 ,  G06T7/194 ,  G06T7/70

CPC classification number: G06T5/005 ,  G06F3/04845 ,  G06T5/002 ,  G06T7/11 ,  G06T7/194 ,  G06T7/70 ,  G06T2200/24 ,  G06T2207/20021 ,  G06T2207/20084 ,  G06T2207/20092

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via multi-layered scene completion techniques facilitated by artificial intelligence. For instance, in some embodiments, the disclosed systems receive a digital image portraying a first object and a second object against a background, where the first object occludes a portion of the second object. Additionally, the disclosed systems pre-process the digital image to generate a first content fill for the portion of the second object occluded by the first object and a second content fill for a portion of the background occluded by the second object. After pre-processing, the disclosed systems detect one or more user interactions to move or delete the first object from the digital image. The disclosed systems further modify the digital image by moving or deleting the first object and exposing the first content fill for the portion of the second object.

公开(公告)号：US10963759B2

公开(公告)日：2021-03-30

申请号：US16417115

申请日：2019-05-20

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Mai Long ,  Jonathan Brandt ,  Hailin Jin ,  Chen Fang

IPC: G06K9/66 ,  G06F16/532 ,  G06K9/46 ,  G06K9/62 ,  G06K9/72 ,  G06N3/04 ,  G06F16/583 ,  G06K9/52 ,  G06N3/08

Abstract: The present disclosure includes methods and systems for searching for digital visual media based on semantic and spatial information. In particular, one or more embodiments of the disclosed systems and methods identify digital visual media displaying targeted visual content in a targeted region based on a query term and a query area provide via a digital canvas. Specifically, the disclosed systems and methods can receive user input of a query term and a query area and provide the query term and query area to a query neural network to generate a query feature set. Moreover, the disclosed systems and methods can compare the query feature set to digital visual media feature sets. Further, based on the comparison, the disclosed systems and methods can identify digital visual media portraying targeted visual content corresponding to the query term within a targeted region corresponding to the query area.

公开(公告)号：US10664719B2

公开(公告)日：2020-05-26

申请号：US15043174

申请日：2016-02-12

Applicant: ADOBE INC.

Inventor： Zhe Lin ,  Xiaohui Shen ,  Jonathan Brandt ,  Jianming Zhang ,  Chen Fang

IPC: G06K9/62 ,  G06K9/46 ,  G06F16/583 ,  G06N20/10 ,  G06F16/51 ,  G06F16/28 ,  G06F16/2457 ,  G06N3/04 ,  G06N3/08

Abstract: Embodiments of the present invention provide an automated image tagging system that can predict a set of tags, along with relevance scores, that can be used for keyword-based image retrieval, image tag proposal, and image tag auto-completion based on user input. Initially, during training, a clustering technique is utilized to reduce cluster imbalance in the data that is input into a convolutional neural network (CNN) for training feature data. In embodiments, the clustering technique can also be utilized to compute data point similarity that can be utilized for tag propagation (to tag untagged images). During testing, a diversity based voting framework is utilized to overcome user tagging biases. In some embodiments, bigram re-weighting can down-weight a keyword that is likely to be part of a bigram based on a predicted tag set.

公开(公告)号：US10460154B2

公开(公告)日：2019-10-29

申请号：US16049322

申请日：2018-07-30

Applicant: Adobe Inc.

Inventor： Jonathan Brandt ,  Zhe Lin ,  Xiaohui Shen ,  Haoxiang Li

IPC: G06K9/00 ,  G06K9/62 ,  G06K9/66

Abstract: Methods and systems for recognizing people in images with increased accuracy are disclosed. In particular, the methods and systems divide images into a plurality of clusters based on common characteristics of the images. The methods and systems also determine an image cluster to which an image with an unknown person instance most corresponds. One or more embodiments determine a probability that the unknown person instance is each known person instance in the image cluster using a trained cluster classifier of the image cluster. Optionally, the methods and systems determine context weights for each combination of an unknown person instance and each known person instance using a conditional random field algorithm based on a plurality of context cues associated with the unknown person instance and the known person instances. The methods and systems calculate a contextual probability based on the cluster-based probabilities and context weights to identify the unknown person instance.

公开(公告)号：US20240135561A1

公开(公告)日：2024-04-25

申请号：US18320714

申请日：2023-05-19

Applicant: Adobe Inc.

Inventor： Zhihong Ding ,  Scott Cohen ,  Matthew Joss ,  Jianming Zhang ,  Darshan Prasad ,  Celso Gomes ,  Jonathan Brandt

IPC: G06T7/50 ,  G06T5/00 ,  G06V10/26

CPC classification number: G06T7/50 ,  G06T5/005 ,  G06V10/26 ,  G06T2207/20084

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that implement depth-aware object move operations for digital image editing. For instance, in some embodiments, the disclosed systems determine a first object depth for a first object portrayed within a digital image and a second object depth for a second object portrayed within the digital image. Additionally, the disclosed systems move the first object to create an overlap area between the first object and the second object within the digital image. Based on the first object depth and the second object depth, the disclosed systems modify the digital image to occlude the first object or the second object within the overlap area.

公开(公告)号：US11816888B2

公开(公告)日：2023-11-14

申请号：US16853111

申请日：2020-04-20

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Xiaohui Shen ,  Jonathan Brandt ,  Jianming Zhang ,  Chen Fang

IPC: G06F16/51 ,  G06F16/28 ,  G06F16/2457 ,  G06F16/583 ,  G06F18/2113 ,  G06F18/21 ,  G06F18/23213 ,  G06F18/2413 ,  G06N3/045 ,  G06N20/10 ,  G06V20/00 ,  G06V10/762 ,  G06V10/764 ,  G06N3/08

CPC classification number: G06V20/35 ,  G06F16/24578 ,  G06F16/285 ,  G06F16/51 ,  G06F16/583 ,  G06F18/217 ,  G06F18/2113 ,  G06F18/23213 ,  G06F18/24147 ,  G06N3/045 ,  G06N3/08 ,  G06V10/763 ,  G06V10/764 ,  G06N20/10

Abstract: Embodiments of the present invention provide an automated image tagging system that can predict a set of tags, along with relevance scores, that can be used for keyword-based image retrieval, image tag proposal, and image tag auto-completion based on user input. Initially, during training, a clustering technique is utilized to reduce cluster imbalance in the data that is input into a convolutional neural network (CNN) for training feature data. In embodiments, the clustering technique can also be utilized to compute data point similarity that can be utilized for tag propagation (to tag untagged images). During testing, a diversity based voting framework is utilized to overcome user tagging biases. In some embodiments, bigram re-weighting can down-weight a keyword that is likely to be part of a bigram based on a predicted tag set.

公开(公告)号：US11113578B1

公开(公告)日：2021-09-07

申请号：US16847270

申请日：2020-04-13

Applicant: Adobe Inc.

Inventor： Jonathan Brandt ,  Radomir Mech ,  Ning Xu ,  Byungmoon Kim ,  Biao Jia

IPC: G06K9/62 ,  G06N3/08

Abstract: A non-photorealistic image rendering system and related techniques are described herein that train and implement machine learning models to reproduce digital images in accordance with various painting styles and constraints. The image rendering system can include a machine learning system that utilizes actor-critic based reinforcement learning techniques to train painting agents (e.g., models that include one or more neural networks) how to transform images into various artistic styles with minimal loss between the original images and the transformed images. The image rendering system can generate constrained painting agents, which correspond to painting agents that are further trained to reproduce images in accordance with one or more constraints. The constraints may include limitations of the color, width, size, and/or position of brushstrokes within reproduced images. These constrained painting agents may provide users with robust, flexible, and customizable non-photorealistic painting systems.

公开(公告)号：US20190252002A1

公开(公告)日：2019-08-15

申请号：US16395041

申请日：2019-04-25

Applicant: Adobe Inc.

Inventor： Zhihong Ding ,  Zhe Lin ,  Xiaohui Shen ,  Michael Kaplan ,  Jonathan Brandt

IPC: G11B27/11 ,  G06T7/00 ,  G06K9/00 ,  G06K9/32 ,  G11B27/031 ,  G06T11/60

CPC classification number: G11B27/11 ,  G06K9/00744 ,  G06K9/3241 ,  G06T7/97 ,  G06T11/60 ,  G06T2207/10016 ,  G11B27/031 ,  G11B27/28 ,  G11B27/326

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.

公开(公告)号：US10346727B2

公开(公告)日：2019-07-09

申请号：US15429769

申请日：2017-02-10

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Mai Long ,  Jonathan Brandt ,  Hailin Jin ,  Chen Fang

IPC: G06K9/46 ,  G06K9/52 ,  G06K9/62 ,  G06K9/66 ,  G06K9/72 ,  G06N3/08 ,  G06F16/532

Abstract: The present disclosure includes methods and systems for searching for digital visual media based on semantic and spatial information. In particular, one or more embodiments of the disclosed systems and methods identify digital visual media displaying targeted visual content in a targeted region based on a query term and a query area provide via a digital canvas. Specifically, the disclosed systems and methods can receive user input of a query term and a query area and provide the query term and query area to a query neural network to generate a query feature set. Moreover, the disclosed systems and methods can compare the query feature set to digital visual media feature sets. Further, based on the comparison, the disclosed systems and methods can identify digital visual media portraying targeted visual content corresponding to the query term within a targeted region corresponding to the query area.