中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

388 records (took 0.032 seconds)

    Convolutional neural networks with adjustable feature resolutions at runtime
    71.
    发明授权
    Convolutional neural networks with adjustable feature resolutions at runtime 有权

    公开(公告)号：US12079725B2

    公开(公告)日：2024-09-03

    申请号：US16751897

    申请日：2020-01-24

    Applicant: Adobe Inc.

    Inventor： Zhe Lin ,  Yilin Wang ,  Siyuan Qiao ,  Jianming Zhang

    IPC: G06N20/00 ,  G06N3/082

    CPC classification number: G06N3/082 ,  G06N20/00

    Abstract: In some embodiments, an application receives a request to execute a convolutional neural network model. The application determines the computational complexity requirement for the neural network based on the computing resource available on the device. The application further determines the architecture of the convolutional neural network model by determining the locations of down-sampling layers within the convolutional neural network model based on the computational complexity requirement. The application reconfigures the architecture of the convolutional neural network model by moving the down-sampling layers to the determined locations and executes the convolutional neural network model to generate output results.

    Panoptic segmentation refinement network
    72.
    发明授权
    Panoptic segmentation refinement network 有权

    公开(公告)号：US12067730B2

    公开(公告)日：2024-08-20

    申请号：US17495618

    申请日：2021-10-06

    Applicant: ADOBE INC.

    Inventor： Zhe Lin ,  Simon Su Chen ,  Jason Wen-youg Kuen ,  Bo Sun

    IPC: G06K9/00 ,  G06N3/08 ,  G06T7/11 ,  G06T7/194

    CPC classification number: G06T7/194 ,  G06N3/08 ,  G06T7/11

    Abstract: Various disclosed embodiments are directed to refining or correcting individual semantic segmentation/instance segmentation masks that have already been produced by baseline models in order to generate a final coherent panoptic segmentation map. Specifically, a refinement model, such as an encoder-decoder-based neural network, generates or predicts various data objects, such as foreground masks, bounding box offset maps, center maps, center offset maps, and coordinate convolution. This, among other functionality described herein, improves the inaccuracies and computing resource consumption of existing technologies.

    REMOVING DISTRACTING OBJECTS FROM DIGITAL IMAGES
    73.
    发明公开
    REMOVING DISTRACTING OBJECTS FROM DIGITAL IMAGES 审中-公开

    公开(公告)号：US20240171848A1

    公开(公告)日：2024-05-23

    申请号：US18058554

    申请日：2022-11-23

    Applicant: Adobe Inc.

    Inventor： Luis Figueroa ,  Zhihong Ding ,  Scott Cohen ,  Zhe Lin ,  Qing Liu

    IPC: H04N23/63 ,  G06V10/26 ,  G06V10/764 ,  G06V10/82 ,  G06V10/94 ,  H04N5/262

    CPC classification number: H04N23/632 ,  G06V10/273 ,  G06V10/764 ,  G06V10/82 ,  G06V10/945 ,  H04N5/2628

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems provide, for display within a graphical user interface of a client device, a digital image displaying a plurality of objects, the plurality of objects comprising a plurality of different types of objects. The disclosed systems generate, utilizing a segmentation neural network and without user input, an object mask for objects of the plurality of objects. The disclosed systems determine, utilizing a distractor detection neural network, a classification for the objects of the plurality of objects. The disclosed systems remove at least one object from the digital image, based on classifying the at least one object as a distracting object, by deleting the object mask for the at least one object.

    MULTI-MODAL IMAGE EDITING
    75.
    发明公开
    MULTI-MODAL IMAGE EDITING 审中-公开

    公开(公告)号：US20240169622A1

    公开(公告)日：2024-05-23

    申请号：US18057851

    申请日：2022-11-22

    Applicant: ADOBE INC.

    Inventor： Shaoan Xie ,  Zhifei Zhang ,  Zhe Lin ,  Tobias Hinz

    IPC: G06T11/60 ,  G06T7/11 ,  G06T11/00

    CPC classification number: G06T11/60 ,  G06T7/11 ,  G06T11/001 ,  G06T2207/20081 ,  G06T2207/20084

    Abstract: Systems and methods for multi-modal image editing are provided. In one aspect, a system and method for multi-modal image editing includes identifying an image, a prompt identifying an element to be added to the image, and a mask indicating a first region of the image for depicting the element. The system then generates a partially noisy image map that includes noise in the first region and image features from the image in a second region outside the first region. A diffusion model generates a composite image map based on the partially noisy image map and the prompt. In some cases, the composite image map includes the target element in the first region that corresponds to the mask.

    Compressing generative adversarial neural networks
    77.
    发明授权
    Compressing generative adversarial neural networks 有权

    公开(公告)号：US11934958B2

    公开(公告)日：2024-03-19

    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 ,  G06N3/082 ,  G06N3/084 ,  G06T5/00 ,  G06T5/40 ,  G06T7/194

    CPC classification number: G06N3/082 ,  G06N3/084 ,  G06T5/002 ,  G06T5/004 ,  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.

    Finding similar persons in images
    78.
    发明授权
    Finding similar persons in images 有权

    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.

    Reducing architectural complexity of convolutional neural networks via channel pruning
    79.
    发明授权
    Reducing architectural complexity of convolutional neural networks via channel pruning 有权

    公开(公告)号：US11875260B2

    公开(公告)日：2024-01-16

    申请号：US15895795

    申请日：2018-02-13

    Applicant: ADOBE INC.

    Inventor： Xin Lu ,  Zhe Lin ,  Jianbo Ye

    IPC: G06N3/082 ,  G06N3/048

    CPC classification number: G06N3/082 ,  G06N3/048

    Abstract: The architectural complexity of a neural network is reduced by selectively pruning channels. A cost metric for a convolution layer is determined. The cost metric indicates a resource cost per channel for the channels of the layer. Training the neural network includes, for channels of the layer, updating a channel-scaling coefficient based on the cost metric. The channel-scaling coefficient linearly scales the output of the channel. A constant channel is identified based on the channel-scaling coefficients. The neural network is updated by pruning the constant channel. Model weights are updated via a stochastic gradient descent of a training loss function evaluated on training data. The channel-scaling coefficients are updated via an iterative-thresholding algorithm that penalizes a batch normalization loss function based on the cost metric for the layer and a norm of the channel-scaling coefficients. When the layer is batch normalized, the channel-scaling coefficients are batch normalization scaling coefficients.

Patent Agency Ranking