中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

54 records (took 0.021 seconds)

    Restoring degraded digital images through a deep learning framework
    41.
    发明授权
    Restoring degraded digital images through a deep learning framework 有权

    公开(公告)号：US12175641B2

    公开(公告)日：2024-12-24

    申请号：US17338949

    申请日：2021-06-04

    Applicant: Adobe Inc.

    Inventor： Ionut Mironica ,  Yijun Li

    IPC: G06T5/77 ,  G06N3/045 ,  G06N3/08 ,  G06T3/18 ,  G06T5/50 ,  G06T5/70 ,  G06T5/73 ,  G06T7/11

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly restoring degraded digital images utilizing a deep learning framework for repairing local defects, correcting global imperfections, and/or enhancing depicted faces. In particular, the disclosed systems can utilize a defect detection neural network to generate a segmentation map indicating locations of local defects within a digital image. In addition, the disclosed systems can utilize an inpainting algorithm to determine pixels for inpainting the local defects to reduce their appearance. In some embodiments, the disclosed systems utilize a global correction neural network to determine and repair global imperfections. Further, the disclosed systems can enhance one or more faces depicted within a digital image utilizing a face enhancement neural network as well.

    GENERATIVE MODEL FOR MULTI-MODALITY OUTPUTS FROM A SINGLE INPUT
    44.
    发明公开
    GENERATIVE MODEL FOR MULTI-MODALITY OUTPUTS FROM A SINGLE INPUT 审中-公开

    公开(公告)号：US20240135672A1

    公开(公告)日：2024-04-25

    申请号：US17971169

    申请日：2022-10-20

    Applicant: Adobe Inc.

    Inventor： Yijun Li ,  Zhixin Shu ,  Zhen Zhu ,  Krishna Kumar Singh

    IPC: G06V10/70 ,  G06N3/04 ,  G06T11/00 ,  G06T15/08

    CPC classification number: G06V10/70 ,  G06N3/0454 ,  G06T11/001 ,  G06T15/08

    Abstract: An image generation system implements a multi-branch GAN to generate images that each express visually similar content in a different modality. A generator portion of the multi-branch GAN includes multiple branches that are each tasked with generating one of the different modalities. A discriminator portion of the multi-branch GAN includes multiple fidelity discriminators, one for each of the generator branches, and a consistency discriminator, which constrains the outputs generated by the different generator branches to appear visually similar to one another. During training, outputs from each of the fidelity discriminators and the consistency discriminator are used to compute a non-saturating GAN loss. The non-saturating GAN loss is used to refine parameters of the multi-branch GAN during training until model convergence. The trained multi-branch GAN generates multiple images from a single input, where each of the multiple images depicts visually similar content expressed in a different modality.

    Generating synthesized digital images utilizing a multi-resolution generator neural network
    47.
    发明授权
    Generating synthesized digital images utilizing a multi-resolution generator neural network 有权

    公开(公告)号：US11769227B2

    公开(公告)日：2023-09-26

    申请号：US17400426

    申请日：2021-08-12

    Applicant: Adobe Inc.

    Inventor： Yuheng Li ,  Yijun Li ,  Jingwan Lu ,  Elya Shechtman ,  Krishna Kumar Singh

    IPC: G06K9/00 ,  G06T3/40 ,  G06N3/04 ,  G06V10/40 ,  G06V30/262 ,  G06F18/25

    CPC classification number: G06T3/4046 ,  G06F18/253 ,  G06N3/04 ,  G06V10/40 ,  G06V30/274

    Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that generate synthetized digital images via multi-resolution generator neural networks. The disclosed system extracts multi-resolution features from a scene representation to condition a spatial feature tensor and a latent code to modulate an output of a generator neural network. For example, the disclosed systems utilizes a base encoder of the generator neural network to generate a feature set from a semantic label map of a scene. The disclosed system then utilizes a bottom-up encoder to extract multi-resolution features and generate a latent code from the feature set. Furthermore, the disclosed system determines a spatial feature tensor by utilizing a top-down encoder to up-sample and aggregate the multi-resolution features. The disclosed system then utilizes a decoder to generate a synthesized digital image based on the spatial feature tensor and the latent code.

    Automatic makeup transfer using semi-supervised learning
    48.
    发明授权
    Automatic makeup transfer using semi-supervised learning 有权

    公开(公告)号：US11508148B2

    公开(公告)日：2022-11-22

    申请号：US16822878

    申请日：2020-03-18

    Applicant: Adobe Inc.

    Inventor： Yijun Li ,  Zhifei Zhang ,  Richard Zhang ,  Jingwan Lu

    IPC: G09G5/00 ,  G06V20/20 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06V10/56 ,  G06V40/50 ,  G06V40/16

    Abstract: The present disclosure relates to systems, computer-implemented methods, and non-transitory computer readable medium for automatically transferring makeup from a reference face image to a target face image using a neural network trained using semi-supervised learning. For example, the disclosed systems can receive, at a neural network, a target face image and a reference face image, where the target face image is selected by a user via a graphical user interface (GUI) and the reference face image has makeup. The systems transfer, by the neural network, the makeup from the reference face image to the target face image, where the neural network is trained to transfer the makeup from the reference face image to the target face image using semi-supervised learning. The systems output for display the makeup on the target face image.

    FEW-SHOT DIGITAL IMAGE GENERATION USING GAN-TO-GAN TRANSLATION
    49.
    发明申请
    FEW-SHOT DIGITAL IMAGE GENERATION USING GAN-TO-GAN TRANSLATION 有权

    公开(公告)号：US20220254071A1

    公开(公告)日：2022-08-11

    申请号：US17163284

    申请日：2021-01-29

    Applicant: Adobe Inc.

    Inventor： Utkarsh Ojha ,  Yijun Li ,  Richard Zhang ,  Jingwan Lu ,  Elya Shechtman ,  Alexei A. Efros

    IPC: G06T11/00 ,  G06K9/62 ,  G06N3/02

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and efficiently modifying a generative adversarial neural network using few-shot adaptation to generate digital images corresponding to a target domain while maintaining diversity of a source domain and realism of the target domain. In particular, the disclosed systems utilize a generative adversarial neural network with parameters learned from a large source domain. The disclosed systems preserve relative similarities and differences between digital images in the source domain using a cross-domain distance consistency loss. In addition, the disclosed systems utilize an anchor-based strategy to encourage different levels or measures of realism over digital images generated from latent vectors in different regions of a latent space.

    COMPRESSING GENERATIVE ADVERSARIAL NEURAL NETWORKS
    50.
    发明申请
    COMPRESSING GENERATIVE ADVERSARIAL NEURAL NETWORKS 有权

    公开(公告)号：US20220222532A1

    公开(公告)日：2022-07-14

    申请号：US17147912

    申请日：2021-01-13

    Applicant: Adobe Inc.

    Inventor： Zhixin Shu ,  Zhe Lin ,  Yuchen Liu ,  Yijun Li

    IPC: G06N3/08 ,  G06T5/00 ,  G06T5/40 ,  G06T7/194

    Abstract: This disclosure describes one or more embodiments of systems, non-transitory computer-readable media, and methods that utilize channel pruning and knowledge distillation to generate a compact noise-to-image GAN. For example, the disclosed systems prune less informative channels via outgoing channel weights of the GAN. In some implementations, the disclosed systems further utilize content-aware pruning by utilizing a differentiable loss between an image generated by the GAN and a modified version of the image to identify sensitive channels within the GAN during channel pruning. In some embodiments, the disclosed systems utilize knowledge distillation to learn parameters for the pruned GAN to mimic a full-size GAN. In certain implementations, the disclosed systems utilize content-aware knowledge distillation by applying content masks on images generated by both the pruned GAN and its full-size counterpart to obtain knowledge distillation losses between the images for use in learning the parameters for the pruned GAN.

Patent Agency Ranking