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公开(公告)号：US11669566B2
公开(公告)日：2023-06-06
申请号：US17565816
申请日：2021-12-30
Applicant: Adobe Inc.
Inventor： Saeid Motiian , Zhe Lin , Samarth Gulati , Pramod Srinivasan , Jose Ignacio Echevarria Vallespi , Baldo Antonio Faieta
IPC: G06F16/00 , G06F16/583 , G06F17/16 , G06F16/55 , G06F16/532 , G06V10/56 , G06V10/75 , G06V10/762
CPC classification number: G06F16/5838 , G06F16/532 , G06F16/55 , G06F17/16 , G06V10/56 , G06V10/758 , G06V10/763
Abstract: In implementations of multi-resolution color-based image search, an image search system determines a color vector for a query image based on a color histogram of the query image by concatenating two color histograms having different resolutions. The image search system can compute distance measures between the color vector of the query image and color vectors of candidate images. The image search system can select one or more of the candidate images to return based on the distance measures utilizing the distance measures as indication of color similarity of the candidate images to the query image.
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公开(公告)号：US20230154185A1
公开(公告)日：2023-05-18
申请号：US17454740
申请日：2021-11-12
Applicant: ADOBE INC.
Inventor： Jason Wen Yong Kuen , Bo Sun , Zhe Lin , Simon Su Chen
CPC classification number: G06K9/00624 , G06K9/6202 , G06K9/6261 , G06N3/08 , G06T3/4046 , G06T9/002
Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure receive an image having a plurality of object instances; encode the image to obtain image features; decode the image features to obtain object features; generate object detection information based on the object features using an object detection branch, wherein the object detection branch is trained based on a first training set using a detection loss; generate semantic segmentation information based on the object features using a semantic segmentation branch, wherein the semantic segmentation branch is trained based on a second training set different from the first training set using a semantic segmentation loss; and combine the object detection information and the semantic segmentation information to obtain panoptic segmentation information that indicates which pixels of the image correspond to each of the plurality of object instances.
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公开(公告)号：US20230128792A1
公开(公告)日：2023-04-27
申请号：US17589114
申请日：2022-01-31
Applicant: Adobe Inc.
Inventor： Jason Wen Yong Kuen , Su Chen , Scott Cohen , Zhe Lin , Zijun Wei , Jianming Zhang
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates object masks for digital objects portrayed in digital images utilizing a detection-masking neural network pipeline. In particular, in one or more embodiments, the disclosed systems utilize detection heads of a neural network to detect digital objects portrayed within a digital image. In some cases, each detection head is associated with one or more digital object classes that are not associated with the other detection heads. Further, in some cases, the detection heads implement multi-scale synchronized batch normalization to normalize feature maps across various feature levels. The disclosed systems further utilize a masking head of the neural network to generate one or more object masks for the detected digital objects. In some cases, the disclosed systems utilize post-processing techniques to filter out low-quality masks.
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公开(公告)号：US11610393B2
公开(公告)日：2023-03-21
申请号：US17062157
申请日：2020-10-02
Applicant: Adobe Inc.
Inventor： Jason Wen Yong Kuen , Zhe Lin , Jiuxiang Gu
IPC: G06V10/778 , G06K9/62 , G06N3/04 , G06T3/60 , G06T3/40 , G06V10/774
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and efficiently learning parameters of a distilled neural network from parameters of a source neural network utilizing multiple augmentation strategies. For example, the disclosed systems can generate lightly augmented digital images and heavily augmented digital images. The disclosed systems can further learn parameters for a source neural network from the lightly augmented digital images. Moreover, the disclosed systems can learn parameters for a distilled neural network from the parameters learned for the source neural network. For example, the disclosed systems can compare classifications of heavily augmented digital images generated by the source neural network and the distilled neural network to transfer learned parameters from the source neural network to the distilled neural network via a knowledge distillation loss function.
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25.发明授权公开(公告)号：US11593948B2
公开(公告)日：2023-02-28
申请号：US17177595
申请日：2021-02-17
Applicant: Adobe Inc.
Inventor： Qihang Yu , Jianming Zhang , He Zhang , Yilin Wang , Zhe Lin , Ning Xu
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a progressive refinement network to refine alpha mattes generated utilizing a mask-guided matting neural network. In particular, the disclosed systems can use the matting neural network to process a digital image and a coarse guidance mask to generate alpha mattes at discrete neural network layers. In turn, the disclosed systems can use the progressive refinement network to combine alpha mattes and refine areas of uncertainty. For example, the progressive refinement network can combine a core alpha matte corresponding to more certain core regions of a first alpha matte and a boundary alpha matte corresponding to uncertain boundary regions of a second, higher resolution alpha matte. Based on the combination of the core alpha matte and the boundary alpha matte, the disclosed systems can generate a final alpha matte for use in image matting processes.
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Patent Agency Ranking
公开(公告)号：US20230042221A1
公开(公告)日：2023-02-09
申请号：US17384109
申请日：2021-07-23
Applicant: Adobe Inc.
Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that perform language guided digital image editing utilizing a cycle-augmentation generative-adversarial neural network (CAGAN) that is augmented using a cross-modal cyclic mechanism. For example, the disclosed systems generate an editing description network that generates language embeddings which represent image transformations applied between a digital image and a modified digital image. The disclosed systems can further train a GAN to generate modified images by providing an input image and natural language embeddings generated by the editing description network (representing various modifications to the digital image from a ground truth modified image). In some instances, the disclosed systems also utilize an image request attention approach with the GAN to generate images that include adaptive edits in different spatial locations of the image.
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公开(公告)号：US11494886B2
公开(公告)日：2022-11-08
申请号：US16888473
申请日：2020-05-29
Applicant: ADOBE INC.
Inventor： Akhilesh Kumar , Zhe Lin , William Lawrence Marino
Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for detecting and classifying an exposure defect in an image using neural networks trained via a limited amount of labeled training images. An image may be applied to a first neural network to determine whether the images includes an exposure defect. Detected defective image may be applied to a second neural network to determine an exposure defect classification for the image. The exposure defect classification can includes severe underexposure, medium underexposure, mild underexposure, mild overexposure, medium overexposure, severe overexposure, and/or the like. The image may be presented to a user along with the exposure defect classification.
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公开(公告)号：US20220237826A1
公开(公告)日：2022-07-28
申请号：US17658799
申请日：2022-04-11
Applicant: Adobe Inc.
Inventor： Zhihong Ding , Scott Cohen , Zhe Lin , Mingyang Ling
Abstract: The present disclosure relates to a color classification system that accurately classifies objects in digital images based on color. In particular, in one or more embodiments, the color classification system utilizes a multidimensional color space and one or more color mappings to match objects to colors. Indeed, the color classification system can accurately and efficiently detect the color of an object utilizing one or more color similarity regions generated in the multidimensional color space.
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公开(公告)号：US20220164380A1
公开(公告)日：2022-05-26
申请号：US17104745
申请日：2020-11-25
Applicant: Adobe Inc.
Inventor： Zhe Lin , Shabnam Ghadar , Saeid Motiian , Ratheesh Kalarot , Baldo Faieta , Alireza Zaeemzadeh
IPC: G06F16/583 , G06F16/532 , G06F16/538 , G06F16/54 , G06F16/56 , G06N20/00
Abstract: A query image is received, along with a query to initiate a search process to find other images based on the query image. The query includes a preference value associated with an attribute, the preference value indicative of a level of emphasis to be placed on the attribute during the search. A full query vector, which is within a first dimensional space and representative of the query image, is generated. The full query vector is projected to a reduced dimensional space having a dimensionality lower than the first dimensional space, to generate a query vector. An attribute direction corresponding to the attribute is identified. A plurality of candidate vectors of the reduced dimensional space is searched, based on the attribute direction, the query vector, and the preference value, to identify a target vector of the plurality of candidate vectors. A target image, representative of the target vector, is displayed.
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公开(公告)号：US20220156992A1
公开(公告)日：2022-05-19
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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