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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.
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2.发明授权公开(公告)号：US10915798B1
公开(公告)日：2021-02-09
申请号：US15980636
申请日：2018-05-15
Applicant: ADOBE INC.
Inventor： Jianming Zhang , Rameswar Panda , Haoxiang Li , Joon-Young Lee , Xin Lu
Abstract: Disclosed herein are embodiments of systems, methods, and products for a webly supervised training of a convolutional neural network (CNN) to predict emotion in images. A computer may query one or more image repositories using search keywords generated based on the tertiary emotion classes of Parrott's emotion wheel. The computer may filter images received in response to the query to generate a weakly labeled training dataset labels associated with the images that are noisy or wrong may be cleaned prior to training of the CNN. The computer may iteratively train the CNN leveraging the hierarchy of emotion classes by increasing the complexity of the labels (tags) for each iteration. Such curriculum guided training may generate a trained CNN that is more accurate than the conventionally trained neural networks.
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公开(公告)号：US20200019758A1
公开(公告)日：2020-01-16
申请号：US16036757
申请日：2018-07-16
Applicant: ADOBE INC.
Inventor： Haoxiang Li , Zhe Lin , Muhammad Abdullah Jamal
Abstract: Methods and systems are provided for generating a facial recognition system. A facial recognition system can be implemented using a meta-model based on a trained neural network. A neural network can be trained as multiple classifiers that identify individuals using a small number of images of the individual's face. A meta-model can learn from the neural networks to be capable to identify an individual based on a small number of images. In this way, the facial recognition system uses the meta-model that learns from the neural network trained to identify an individual based on a small number of images. Such a facial recognition system is tested to determine any misidentification for fine-tuning the system. A facial recognition system implemented using such a meta-model is capable of adapting the model to learn identities entered into the system using only a small number of images to enroll an identity into the system.
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Patent Agency Ranking
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
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.
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公开(公告)号：US11709462B2
公开(公告)日：2023-07-25
申请号：US15894688
申请日：2018-02-12
Applicant: ADOBE INC.
Inventor： Haoxiang Li , Yinan Zhang
CPC classification number: G05B13/027 , G05B17/02 , G06N3/08 , G06N20/00
Abstract: The training of a learning agent to provide real-time control of an object is disclosed. Training of the learning agent and training of a corresponding pioneer agent are iteratively alternated. The training of the learning and pioneer agents is under the supervision of a supervisor agent. The training of the learning agent provides feedback for subsequent training of the pioneer agent. The training of the pioneer agent provides feedback for subsequent training of the learning agent. During the training, a supervisor coefficient modulates the influence of the supervisor agent. As agents are trained, the influence of the supervisor agent is decayed. The training of the learning agent, under a first level of supervisor influence, includes real-time control of the object. The subsequent training of the pioneer agent, under a reduced level of supervisor influence, includes replay of training data accumulated during the real-time control of the object.
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公开(公告)号：US10832036B2
公开(公告)日：2020-11-10
申请号：US16036757
申请日：2018-07-16
Applicant: ADOBE INC.
Inventor： Haoxiang Li , Zhe Lin , Muhammad Abdullah Jamal
Abstract: Methods and systems are provided for generating a facial recognition system. A facial recognition system can be implemented using a meta-model based on a trained neural network. A neural network can be trained as multiple classifiers that identify individuals using a small number of images of the individual's face. A meta-model can learn from the neural networks to be capable to identify an individual based on a small number of images. In this way, the facial recognition system uses the meta-model that learns from the neural network trained to identify an individual based on a small number of images. Such a facial recognition system is tested to determine any misidentification for fine-tuning the system. A facial recognition system implemented using such a meta-model is capable of adapting the model to learn identities entered into the system using only a small number of images to enroll an identity into the system.
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公开(公告)号：US20190258925A1
公开(公告)日：2019-08-22
申请号：US15900351
申请日：2018-02-20
Applicant: Adobe Inc.
Inventor： Haoxiang Li , Xiaohui Shen , Xiangyun Zhao
IPC: G06N3/08
Abstract: This disclosure covers methods, non-transitory computer readable media, and systems that learn attribute attention projections for attributes of digital images and parameters for an attention controlled neural network. By iteratively generating and comparing attribute-modulated-feature vectors from digital images, the methods, non-transitory computer readable media, and systems update attribute attention projections and parameters indicating either one (or both) of a correlation between some attributes of digital images and a discorrelation between other attributes of digital images. In certain embodiments, the methods, non-transitory computer readable media, and systems use the attribute attention projections in an attention controlled neural network as part of performing one or more tasks.
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公开(公告)号：US20190250568A1
公开(公告)日：2019-08-15
申请号：US15894688
申请日：2018-02-12
Applicant: ADOBE INC.
Inventor： Haoxiang Li , Yinan Zhang
CPC classification number: G05B13/027 , G05B17/02 , G06N3/08 , G06N20/00
Abstract: The training of a learning agent to provide real-time control of an object is disclosed. Training of the learning agent and training of a corresponding pioneer agent are iteratively alternated. The training of the learning and pioneer agents is under the supervision of a supervisor agent. The training of the learning agent provides feedback for subsequent training of the pioneer agent. The training of the pioneer agent provides feedback for subsequent training of the learning agent. During the training, a supervisor coefficient modulates the influence of the supervisor agent. As agents are trained, the influence of the supervisor agent is decayed. The training of the learning agent, under a first level of supervisor influence, includes real-time control of the object. The subsequent training of the pioneer agent, under a reduced level of supervisor influence, includes replay of training data accumulated during the real-time control of the object.
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