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公开(公告)号:US20200334487A1
公开(公告)日:2020-10-22
申请号:US16919383
申请日:2020-07-02
Applicant: Adobe Inc.
Inventor: Delun Du , Zhe Lin , Baldo Faieta
Abstract: The present disclosure is directed toward systems and methods for detecting an object in an input image based on a target object keyword. For example, one or more embodiments described herein generate a heat map of the input image based on the target object keyword and generate various bounding boxes based on a pixel analysis of the heat map. One or more embodiments described herein then utilize the various bounding boxes to determine scores for generated object location proposals in order to provide a highest scoring object location proposal overlaid on the input image.
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公开(公告)号:US10475098B2
公开(公告)日:2019-11-12
申请号:US14827645
申请日:2015-08-17
Applicant: Adobe Inc.
Inventor: Zeke Koch , Baldo Faieta , Jen-Chan Chien , Mark M. Randall , Olivier Sirven , Philipp Koch , Dennis G. Nicholson
IPC: G06F7/00 , G06F17/30 , G06Q30/06 , G06F21/16 , G06Q50/18 , G06F16/9535 , G06F16/583 , G06F16/58
Abstract: Content creation suggestion techniques are described. In one or more implementations, techniques are implemented to generate suggestions that are usable to guide creative professionals in the creation of content such as images, video, sound, multimedia, and so forth. A variety of techniques are usable to generate suggestions for the content professionals. In a first such example, suggestions are based on shared characteristics of images obtained by users of a content sharing service, e.g., licensed by the users. In another example, suggestions are generated by the content sharing service based on keywords used to locate the images. In a further example, suggestions are generated based on data described communications performed using social network services. In yet another example, recognition of failure of search is used to generate suggestions. A variety of other examples are also contemplated and described herein.
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公开(公告)号:US20190163766A1
公开(公告)日:2019-05-30
申请号:US15824836
申请日:2017-11-28
Applicant: ADOBE INC.
Inventor: Samarth Gulati , Brett Butterfield , Baldo Faieta , Bernard James Kerr , Kent Andrew Edmonds
IPC: G06F17/30
Abstract: Systems and methods for searching digital content, such as digital images, are disclosed. A method includes receiving a first search constraint and generating search results based on the first search constraint. A search constraint includes search values or criteria. The search results include a ranked set of digital images. A second search constraint and a weight value associated with the second search constraint are received. The search results are updated based on the second search constraint and the weight value. The updated search results are provided to a user. Updating the search results includes determining a ranking (or a re-ranking) for each item of content included in the search results based on the first search constraint, the second search constraint, and the weight value. Re-ranking the search results may further be based on a weight value associated with the first search constraint, such as a default or maximum weight value.
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公开(公告)号:US20250117126A1
公开(公告)日:2025-04-10
申请号:US18904517
申请日:2024-10-02
Applicant: ADOBE INC.
Inventor: Ajinkya Gorakhnath Kale , Alvin Ghouas , Baldo Faieta , Evan Benjamin Shimizu , Jeremy Jay Joachim , Tomasz Opasinski
IPC: G06F3/04847 , G06F3/0486 , G06F16/43 , G06T11/00
Abstract: A method, apparatus, non-transitory computer readable medium, and system for media processing includes obtaining a variation parameter and a number of variations, identifying a first variation input and a second variation input for the variation parameter, and obtaining a first media content item and a second media content item based on the first variation input and the second variation input, respectively. The first media content item and the second media content item vary from each other with respect to the variation parameter. The method, apparatus, non-transitory computer readable medium, and system for media processing further includes displaying the first media content item and the second media content item in a grid comprising a grid size based on the number of variations.
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公开(公告)号:US12223439B2
公开(公告)日:2025-02-11
申请号:US17190668
申请日:2021-03-03
Applicant: ADOBE INC.
Inventor: Xin Yuan , Zhe Lin , Jason Wen Yong Kuen , Jianming Zhang , Yilin Wang , Ajinkya Kale , Baldo Faieta
Abstract: Systems and methods for multi-modal representation learning are described. One or more embodiments provide a visual representation learning system trained using machine learning techniques. For example, some embodiments of the visual representation learning system are trained using cross-modal training tasks including a combination of intra-modal and inter-modal similarity preservation objectives. In some examples, the training tasks are based on contrastive learning techniques.
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公开(公告)号:US11915520B2
公开(公告)日:2024-02-27
申请号:US17902349
申请日:2022-09-02
Applicant: Adobe Inc.
Inventor: Saeid Motiian , Zhe Lin , Shabnam Ghadar , Baldo Faieta
IPC: G06V40/16 , G06V30/194 , G06V40/10 , G06F18/00 , G06F18/20
CPC classification number: G06V40/172 , G06F18/00 , G06F18/29 , G06V30/194 , G06V40/10
Abstract: Embodiments are disclosed for finding similar persons in images. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an image query, the image query including an input image that includes a representation of a person, generating a first cropped image including a representation of the person's face and a second cropped image including a representation of the person's body, generating an image embedding for the input image by combining a face embedding corresponding to the first cropped image and a body embedding corresponding to the second cropped image, and querying an image repository in embedding space by comparing the image embedding to a plurality of image embeddings associated with a plurality of images in the image repository to obtain one or more images based on similarity to the input image in the embedding space.
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公开(公告)号:US11875221B2
公开(公告)日:2024-01-16
申请号:US17468476
申请日:2021-09-07
Applicant: Adobe Inc.
Inventor: Wei-An Lin , Baldo Faieta , Cameron Smith , Elya Shechtman , Jingwan Lu , Jun-Yan Zhu , Niloy Mitra , Ratheesh Kalarot , Richard Zhang , Shabnam Ghadar , Zhixin Shu
IPC: G06N3/08 , G06F3/04845 , G06F3/04847 , G06T11/60 , G06T3/40 , G06N20/20 , G06T5/00 , G06T5/20 , G06T3/00 , G06T11/00 , G06F18/40 , G06F18/211 , G06F18/214 , G06F18/21 , G06N3/045
CPC classification number: G06N3/08 , G06F3/04845 , G06F3/04847 , G06F18/211 , G06F18/214 , G06F18/2163 , G06F18/40 , G06N3/045 , G06N20/20 , G06T3/0006 , G06T3/0093 , G06T3/40 , G06T3/4038 , G06T3/4046 , G06T5/005 , G06T5/20 , G06T11/001 , G06T11/60 , G06T2207/10024 , G06T2207/20081 , G06T2207/20084 , G06T2207/20221 , G06T2210/22
Abstract: Systems and methods generate a filtering function for editing an image with reduced attribute correlation. An image editing system groups training data into bins according to a distribution of a target attribute. For each bin, the system samples a subset of the training data based on a pre-determined target distribution of a set of additional attributes in the training data. The system identifies a direction in the sampled training data corresponding to the distribution of the target attribute to generate a filtering vector for modifying the target attribute in an input image, obtains a latent space representation of an input image, applies the filtering vector to the latent space representation of the input image to generate a filtered latent space representation of the input image, and provides the filtered latent space representation as input to a neural network to generate an output image with a modification to the target attribute.
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公开(公告)号:US20230162407A1
公开(公告)日:2023-05-25
申请号:US17455796
申请日:2021-11-19
Applicant: ADOBE INC.
Inventor: Ratheesh Kalarot , Timothy M. Converse , Shabnam Ghadar , John Thomas Nack , Jingwan Lu , Elya Shechtman , Baldo Faieta , Akhilesh Kumar
CPC classification number: G06T11/00 , G06K9/00288 , G06K9/00268 , G06N3/08
Abstract: The present disclosure describes systems and methods for image processing. Embodiments of the present disclosure include an image processing apparatus configured to generate modified images (e.g., synthetic faces) by conditionally changing attributes or landmarks of an input image. A machine learning model of the image processing apparatus encodes the input image to obtain a joint conditional vector that represents attributes and landmarks of the input image in a vector space. The joint conditional vector is then modified, according to the techniques described herein, to form a latent vector used to generate a modified image. In some cases, the machine learning model is trained using a generative adversarial network (GAN) with a normalization technique, followed by joint training of a landmark embedding and attribute embedding (e.g., to reduce inference time).
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公开(公告)号:US20230137774A1
公开(公告)日:2023-05-04
申请号:US17453595
申请日:2021-11-04
Applicant: ADOBE INC.
Inventor: Baldo Faieta , Ajinkya Gorakhnath Kale , Pranav Vineet Aggarwal , Naveen Marri , Saeid Motiian , Tracy Holloway King , Alex Filipkowski , Shabnam Ghadar
IPC: G06F16/583 , G06F16/58 , G06F16/538 , G06F40/295 , G06F16/535 , G06N3/08
Abstract: Systems and methods for image retrieval are described. Embodiments of the present disclosure receive a search query from a user; extract an entity and a color phrase describing the entity from the search query; generate an entity color embedding in a color embedding space from the color phrase using a multi-modal color encoder; identify an image in a database based on metadata for the image including an object label corresponding to the extracted entity and an object color embedding in the color embedding space corresponding to the object label; and provide image information for the image to the user based on the metadata.
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公开(公告)号:US11615567B2
公开(公告)日:2023-03-28
申请号:US16952008
申请日:2020-11-18
Applicant: Adobe Inc.
Inventor: Midhun Harikumar , Pranav Aggarwal , Baldo Faieta , Ajinkya Kale , Zhe Lin
Abstract: A non-transitory computer-readable medium includes program code that is stored thereon. The program code is executable by one or more processing devices for performing operations including generating, by a model that includes trainable components, a learned image representation of a target image. The operations further include generating, by a text embedding model, a text embedding of a text query. The text embedding and the learned image representation of the target image are in a same embedding space. Additionally, the operations include generating a class activation map of the target image by, at least, convolving the learned image representation of the target image with the text embedding of the text query. Moreover, the operations include generating an object-segmented image using the class activation map of the target image.
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