公开(公告)号：US12260557B2

公开(公告)日：2025-03-25

申请号：US17838995

申请日：2022-06-13

Applicant: ADOBE INC.

Inventor： Zijun Wei ,  Yilin Wang ,  Jianming Zhang ,  He Zhang

IPC: G06K9/00 ,  G06T7/11 ,  G06T7/136 ,  G06V10/46 ,  G06V10/764

Abstract: An image processing system generates an image mask from an image. The image is processed by an object detector to identify a region having an object, and the region is classified based on an object type of the object. A masking pipeline is selected from a number of masking pipelines based on the classification of the region. The region is processed using the masking pipeline to generate a region mask. An image mask for the image is generated using the region mask.

公开(公告)号：US11983632B2

公开(公告)日：2024-05-14

申请号：US18309367

申请日：2023-04-28

Applicant: Adobe Inc.

Inventor： Shikun Liu ,  Zhe Lin ,  Yilin Wang ,  Jianming Zhang ,  Federico Perazzi

IPC: G06N3/082 ,  G06N3/04

CPC classification number: G06N3/082 ,  G06N3/04

Abstract: The disclosure describes one or more implementations of a neural network architecture pruning system that automatically and progressively prunes neural networks. For instance, the neural network architecture pruning system can automatically reduce the size of an untrained or previously-trained neural network without reducing the accuracy of the neural network. For example, the neural network architecture pruning system jointly trains portions of a neural network while progressively pruning redundant subsets of the neural network at each training iteration. In many instances, the neural network architecture pruning system increases the accuracy of the neural network by progressively removing excess or redundant portions (e.g., channels or layers) of the neural network. Further, by removing portions of a neural network, the neural network architecture pruning system can increase the efficiency of the neural network.

公开(公告)号：US20230401716A1

公开(公告)日：2023-12-14

申请号：US17806312

申请日：2022-06-10

Applicant: ADOBE INC.

Inventor： Yilin Wang ,  Chenglin Yang ,  Jianming Zhang ,  He Zhang ,  Zijun Wei ,  Zhe Lin

IPC: G06T7/10 ,  G06T11/60 ,  G06N3/08

CPC classification number: G06T7/10 ,  G06T11/60 ,  G06N3/088

Abstract: Systems and methods for image segmentation are described. Embodiments of the present disclosure receive an image depicting an object; generate image features for the image by performing a convolutional self-attention operation that outputs a plurality of attention-weighted values for a convolutional kernel applied at a position of a sliding window on the image; and generate label data that identifies the object based on the image features.

公开(公告)号：US11710042B2

公开(公告)日：2023-07-25

申请号：US16782793

申请日：2020-02-05

Applicant: Adobe Inc.

Inventor： Shikun Liu ,  Zhe Lin ,  Yilin Wang ,  Jianming Zhang ,  Federico Perazzi

IPC: G06N3/04 ,  G06N3/082

CPC classification number: G06N3/082 ,  G06N3/04

Abstract: The present disclosure relates to shaping the architecture of a neural network. For example, the disclosed systems can provide a neural network shaping mechanism for at least one sampling layer of a neural network. The neural network shaping mechanism can include a learnable scaling factor between a sampling rate of the at least one sampling layer and an additional sampling function. The disclosed systems can learn the scaling factor based on a dataset while jointly learning the network weights of the neural network. Based on the learned scaling factor, the disclosed systems can shape the architecture of the neural network by modifying the sampling rate of the at least one sampling layer.

公开(公告)号：US11663481B2

公开(公告)日：2023-05-30

申请号：US16799191

申请日：2020-02-24

Applicant: Adobe Inc.

Inventor： Shikun Liu ,  Zhe Lin ,  Yilin Wang ,  Jianming Zhang ,  Federico Perazzi

IPC: G06N3/082 ,  G06N3/04

CPC classification number: G06N3/082 ,  G06N3/04

Abstract: The disclosure describes one or more implementations of a neural network architecture pruning system that automatically and progressively prunes neural networks. For instance, the neural network architecture pruning system can automatically reduce the size of an untrained or previously-trained neural network without reducing the accuracy of the neural network. For example, the neural network architecture pruning system jointly trains portions of a neural network while progressively pruning redundant subsets of the neural network at each training iteration. In many instances, the neural network architecture pruning system increases the accuracy of the neural network by progressively removing excess or redundant portions (e.g., channels or layers) of the neural network. Further, by removing portions of a neural network, the neural network architecture pruning system can increase the efficiency of the neural network.

公开(公告)号：US20210357684A1

公开(公告)日：2021-11-18

申请号：US15930539

申请日：2020-05-13

Applicant: Adobe Inc.

Inventor： Sohrab Amirghodsi ,  Zhe Lin ,  Yilin Wang ,  Tianshu Yu ,  Connelly Barnes ,  Elya Shechtman

IPC: G06K9/62 ,  G06N3/08 ,  G06N20/00 ,  G06F17/18

Abstract: A panoptic labeling system includes a modified panoptic labeling neural network (“modified PLNN”) that is trained to generate labels for pixels in an input image. The panoptic labeling system generates modified training images by combining training images with mask instances from annotated images. The modified PLNN determines a set of labels representing categories of objects depicted in the modified training images. The modified PLNN also determines a subset of the labels representing categories of objects depicted in the input image. For each mask pixel in a modified training image, the modified PLNN calculates a probability indicating whether the mask pixel has the same label as an object pixel. The modified PLNN generates a mask label for each mask pixel, based on the probability. The panoptic labeling system provides the mask label to, for example, a digital graphics editing system that uses the labels to complete an infill operation.

公开(公告)号：US10769764B2

公开(公告)日：2020-09-08

申请号：US16271058

申请日：2019-02-08

Applicant: Adobe Inc.

Inventor： Chen Fang ,  Zhe Lin ,  Zhaowen Wang ,  Yulun Zhang ,  Yilin Wang ,  Jimei Yang

IPC: G06T7/40 ,  G06T5/50 ,  G06T3/40 ,  G06T7/11 ,  G06T11/00 ,  G06T3/00

Abstract: A style of a digital image is transferred to another digital image of arbitrary resolution. A high-resolution (HR) content image is segmented into several low-resolution (LR) patches. The resolution of a style image is matched to have the same resolution as the LR content image patches. Style transfer is then performed on a patch-by-patch basis using, for example, a pair of feature transforms—whitening and coloring. The patch-by-patch style transfer process is then repeated at several increasing resolutions, or scale levels, of both the content and style images. The results of the style transfer at each scale level are incorporated into successive scale levels up to and including the original HR scale. As a result, style transfer can be performed with images having arbitrary resolutions to produce visually pleasing results with good spatial consistency.

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

IPC: G06N5/04 ,  G06N3/08 ,  G06N20/00

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.

公开(公告)号：US20240404188A1

公开(公告)日：2024-12-05

申请号：US18205279

申请日：2023-06-02

Applicant: Adobe Inc.

Inventor： He Zhang ,  Zijun Wei ,  Zhixin Shu ,  Yiqun Mei ,  Yilin Wang ,  Xuaner Zhang ,  Shi Yan ,  Jianming Zhang

IPC: G06T15/50 ,  G06T7/11 ,  G06T15/04 ,  G06T15/60 ,  G06T15/80

Abstract: In accordance with the described techniques, a portrait relighting system receives user input defining one or more markings drawn on a portrait image. Using one or more machine learning models, the portrait relighting system generates an albedo representation of the portrait image by removing lighting effects from the portrait image. Further, the portrait relighting system generates a shading map of the portrait image using the one or more machine learning models by designating the one or more markings as a lighting condition, and applying the lighting condition to a geometric representation of the portrait image. The one or more machine learning models are further employed to generate a relit portrait image based on the albedo representation and the shading map.

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.