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    UTILIZING IMPLICIT NEURAL REPRESENTATIONS TO PARSE VISUAL COMPONENTS OF SUBJECTS DEPICTED WITHIN VISUAL CONTENT
    2.
    发明申请
    UTILIZING IMPLICIT NEURAL REPRESENTATIONS TO PARSE VISUAL COMPONENTS OF SUBJECTS DEPICTED WITHIN VISUAL CONTENT 有权

    公开(公告)号：US20240378912A1

    公开(公告)日：2024-11-14

    申请号：US18316617

    申请日：2023-05-12

    Applicant: Adobe Inc.

    Inventor： Mausoom Sarkar ,  Nikitha S R ,  Mayur Hemani ,  Rishabh Jain ,  Balaji Krishnamurthy

    IPC: G06V20/70 ,  G06T3/40 ,  G06T7/11 ,  G06V10/46 ,  G06V10/764 ,  G06V10/77 ,  G06V10/774 ,  G06V10/82 ,  G06V40/16

    Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that utilize a local implicit image function neural network to perform image segmentation with a continuous class label probability distribution. For example, the disclosed systems utilize a local-implicit-image-function (LIIF) network to learn a mapping from an image to its semantic label space. In some instances, the disclosed systems utilize an image encoder to generate an image vector representation from an image. Subsequently, in one or more implementations, the disclosed systems utilize the image vector representation with a LIIF network decoder that generates a continuous probability distribution in a label space for the image to create a semantic segmentation mask for the image. Moreover, in some embodiments, the disclosed systems utilize the LIIF-based segmentation network to generate segmentation masks at different resolutions without changes in an input resolution of the segmentation network.

    Similarity propagation for one-shot and few-shot image segmentation
    3.
    发明授权
    Similarity propagation for one-shot and few-shot image segmentation 有权

    公开(公告)号：US11367271B2

    公开(公告)日：2022-06-21

    申请号：US16906954

    申请日：2020-06-19

    Applicant: ADOBE INC.

    Inventor： Mayur Hemani ,  Siddhartha Gairola ,  Ayush Chopra ,  Balaji Krishnamurthy ,  Jonas Dahl

    IPC: G06K9/62 ,  G06V10/46 ,  G06K9/46

    Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for one-shot and few-shot image segmentation on classes of objects that were not represented during training. In some embodiments, a dual prediction scheme may be applied in which query and support masks are jointly predicted using a shared decoder, which aids in similarity propagation between the query and support features. Additionally or alternatively, foreground and background attentive fusion may be applied to utilize cues from foreground and background feature similarities between the query and support images. Finally, to prevent overfitting on class-conditional similarities across training classes, input channel averaging may be applied for the query image during training. Accordingly, the techniques described herein may be used to achieve state-of-the-art performance for both one-shot and few-shot segmentation tasks.

    SIMILARITY PROPAGATION FOR ONE-SHOT AND FEW-SHOT IMAGE SEGMENTATION
    4.
    发明申请
    SIMILARITY PROPAGATION FOR ONE-SHOT AND FEW-SHOT IMAGE SEGMENTATION 有权

    公开(公告)号：US20210397876A1

    公开(公告)日：2021-12-23

    申请号：US16906954

    申请日：2020-06-19

    Applicant: ADOBE INC.

    Inventor： Mayur Hemani ,  Siddhartha Gairola ,  Ayush Chopra ,  Balaji Krishnamurthy ,  Jonas Dahl

    IPC: G06K9/46 ,  G06K9/62

    Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for one-shot and few-shot image segmentation on classes of objects that were not represented during training. In some embodiments, a dual prediction scheme may be applied in which query and support masks are jointly predicted using a shared decoder, which aids in similarity propagation between the query and support features. Additionally or alternatively, foreground and background attentive fusion may be applied to utilize cues from foreground and background feature similarities between the query and support images. Finally, to prevent overfitting on class-conditional similarities across training classes, input channel averaging may be applied for the query image during training. Accordingly, the techniques described herein may be used to achieve state-of-the-art performance for both one-shot and few-shot segmentation tasks.

    Cloth warping using multi-scale patch adversarial loss
    5.
    发明授权
    Cloth warping using multi-scale patch adversarial loss 有权

    公开(公告)号：US11080817B2

    公开(公告)日：2021-08-03

    申请号：US16673574

    申请日：2019-11-04

    Applicant: Adobe Inc.

    Inventor： Kumar Ayush ,  Surgan Jandial ,  Mayur Hemani ,  Balaji Krishnamurthy ,  Ayush Chopra

    IPC: G06T3/00 ,  G06T11/60 ,  G06T7/40

    Abstract: Generating a synthesized image of a person wearing clothing is described. A two-dimensional reference image depicting a person wearing an article of clothing and a two-dimensional image of target clothing in which the person is to be depicted as wearing are received. To generate the synthesized image, a warped image of the target clothing is generated via a geometric matching module, which implements a machine learning model trained to recognize similarities between warped and non-warped clothing images using multi-scale patch adversarial loss. The multi-scale patch adversarial loss is determined by sampling patches of different sizes from corresponding locations of warped and non-warped clothing images. The synthesized image is generated on a per-person basis, such that the target clothing fits the particular body shape, pose, and unique characteristics of the person.

    Cloth Warping Using Multi-Scale Patch Adversarial Loss
    6.
    发明申请
    Cloth Warping Using Multi-Scale Patch Adversarial Loss 有权

    公开(公告)号：US20210133919A1

    公开(公告)日：2021-05-06

    申请号：US16673574

    申请日：2019-11-04

    Applicant: Adobe Inc.

    Inventor： Kumar Ayush ,  Surgan Jandial ,  Mayur Hemani ,  Balaji Krishnamurthy ,  Ayush Chopra

    IPC: G06T3/00 ,  G06T11/60 ,  G06T7/40

    Abstract: Generating a synthesized image of a person wearing clothing is described. A two-dimensional reference image depicting a person wearing an article of clothing and a two-dimensional image of target clothing in which the person is to be depicted as wearing are received. To generate the synthesized image, a warped image of the target clothing is generated via a geometric matching module, which implements a machine learning model trained to recognize similarities between warped and non-warped clothing images using multi-scale patch adversarial loss. The multi-scale patch adversarial loss is determined by sampling patches of different sizes from corresponding locations of warped and non-warped clothing images. The synthesized image is generated on a per-person basis, such that the target clothing fits the particular body shape, pose, and unique characteristics of the person.

    Generating Images for Virtual Try-On and Pose Transfer
    8.
    发明公开
    Generating Images for Virtual Try-On and Pose Transfer 审中-公开

    公开(公告)号：US20230267663A1

    公开(公告)日：2023-08-24

    申请号：US17678237

    申请日：2022-02-23

    Applicant: Adobe Inc.

    Inventor： Ayush Chopra ,  Rishabh Jain ,  Mayur Hemani ,  Balaji Krishnamurthy

    IPC: G06T11/60 ,  G06T7/70 ,  G06T7/11 ,  G06N3/04

    CPC classification number: G06T11/60 ,  G06T7/70 ,  G06T7/11 ,  G06N3/0454

    Abstract: In implementations of systems for generating images for virtual try-on and pose transfer, a computing device implements a generator system to receive input data describing a first digital image that depicts a person in a pose and a second digital image that depicts a garment. Candidate appearance flow maps are computed that warp the garment based on the pose at different pixel-block sizes using a first machine learning model. The generator system generates a warped garment image by combining the candidate appearance flow maps as an aggregate per-pixel displacement map using a convolutional gated recurrent network. A conditional segment mask is predicted that segments portions of a geometry of the person using a second machine learning model. The generator system outputs a digital image that depicts the person in the pose wearing the garment based on the warped garment image and the conditional segmentation mask using a third machine learning model.

    Generating views of three-dimensional models illustrating defects
    9.
    发明授权
    Generating views of three-dimensional models illustrating defects 有权

    公开(公告)号：US10410410B2

    公开(公告)日：2019-09-10

    申请号：US15948528

    申请日：2018-04-09

    Applicant: Adobe Inc.

    Inventor： Naveen Goel ,  Mayur Hemani ,  Harsh Vardhan Chopra ,  Amit Mittal

    IPC: G06T17/00 ,  G06T15/04 ,  G06T15/06 ,  G06K9/62 ,  G06T15/20 ,  G06K9/00 ,  G06K9/46

    Abstract: Systems and methods are disclosed for generating viewpoints and/or digital images of defects in a three-dimensional model. In particular, in one or more embodiments, the disclosed systems and methods generate exterior viewpoints by clustering intersection points between a bounding sphere and rays originating from exterior vertices corresponding to one or more defects. In addition, in one or more embodiments, the disclosed systems and methods generate interior viewpoints by clustering intersection points between one or more medial spheres and rays originating from vertices corresponding to interior vertices corresponding to one or more defects. Furthermore, the disclosed systems and methods can apply colors to vertices corresponding to defects in the three-dimensional model such that adjacent vertices in the three-dimensional model have different colors and are more readily discernable.

    DEBIASING VISION-LANGUAGE MODELS WITH ADDITIVE RESIDUALS
    10.
    发明申请
    DEBIASING VISION-LANGUAGE MODELS WITH ADDITIVE RESIDUALS 有权

    公开(公告)号：US20240395024A1

    公开(公告)日：2024-11-28

    申请号：US18322253

    申请日：2023-05-23

    Applicant: Adobe Inc.

    Inventor： Mayur Hemani ,  Chirag Agarwal ,  Ashish Seth

    IPC: G06V10/776 ,  G06F16/33 ,  G06F40/40 ,  G06V10/74 ,  G06V10/764 ,  G06V10/77 ,  G06V10/82

    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for debiasing vision-language models utilizing additive residual learning. In particular, in one or more embodiments, the disclosed systems generate an encoded image representation of a digital image utilizing an image encoder of a vision-language neural network. Additionally, in some embodiments, the disclosed systems extract a protected attribute encoding from the encoded image representation of the digital image utilizing an additive residual learner. Upon extracting the protected attribute encoding, in some implementations, the disclosed systems determine a debiased image encoding for the digital image by combining the protected attribute encoding and the encoded image representation.

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