-
131.发明申请公开(公告)号：US20190279346A1
公开(公告)日：2019-09-12
申请号：US15914659
申请日：2018-03-07
Applicant: Adobe Inc.
Inventor： Jianming Zhang , Zhe Lin , Xiaohui Shen , Wei-Chih Hung , Joon-Young Lee
Abstract: Certain embodiments involve blending images using neural networks to automatically generate alignment or photometric adjustments that control image blending operations. For instance, a foreground image and a background image data are provided to an adjustment-prediction network that has been trained, using a reward network, to compute alignment or photometric adjustments that optimize blending reward scores. An adjustment action (e.g., an alignment or photometric adjustment) is computed by applying the adjustment-prediction network to the foreground image and the background image data. A target background region is extracted from the background image data by applying the adjustment action to the background image data. The target background region is blended with the foreground image, and the resultant blended image is outputted.
-
公开(公告)号：US10311574B2
公开(公告)日：2019-06-04
申请号：US15853206
申请日：2017-12-22
Applicant: Adobe Inc.
Inventor： Xiaohui Shen , Zhe Lin , Yi-Hsuan Tsai , Kalyan K. Sunkavalli
Abstract: A digital medium environment includes an image processing application that performs object segmentation on an input image. An improved object segmentation method implemented by the image processing application comprises receiving an input image that includes an object region to be segmented by a segmentation process, processing the input image to provide a first segmentation that defines the object region, and processing the first segmentation to provide a second segmentation that provides pixel-wise label assignments for the object region. In some implementations, the image processing application performs improved sky segmentation on an input image containing a depiction of a sky.
-
公开(公告)号：US12260530B2
公开(公告)日：2025-03-25
申请号：US18190544
申请日：2023-03-27
Applicant: Adobe Inc.
Inventor： Krishna Kumar Singh , Yijun Li , Jingwan Lu , Duygu Ceylan Aksit , Yangtuanfeng Wang , Jimei Yang , Tobias Hinz , Qing Liu , Jianming Zhang , Zhe Lin
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 example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.
-
公开(公告)号：US20250029226A1
公开(公告)日：2025-01-23
申请号：US18908531
申请日：2024-10-07
Applicant: Adobe Inc.
Inventor： Akhilesh KUMAR , Zhe Lin , William Lawrence Marino
Abstract: Models for classifying exposure defects in images are provided by training a binary model on a dataset of images labeled to indicate exposure within the images. When trained, the binary model classifies an image based on whether the image includes an exposure defect. A classification model is also trained. The classification model is trained on a dataset of images having exposure defects labeled to indicate exposure scores or exposure defect classifications. When trained, the classification model classifies the image based on a level of exposure. The binary model and the classification model can be stored for identifying and classifying exposure defects within images.
-
公开(公告)号：US20250022252A1
公开(公告)日：2025-01-16
申请号：US18899571
申请日：2024-09-27
Applicant: Adobe Inc.
Inventor： Khoi Pham , Kushal Kafle , Zhe Lin , Zhihong Ding , Scott Cohen , Quan Tran
IPC: G06V10/75 , G06F18/214 , G06F18/25 , G06N3/08
Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that extract multiple attributes from an object portrayed in a digital image utilizing a multi-attribute contrastive classification neural network. For example, the disclosed systems utilize a multi-attribute contrastive classification neural network that includes an embedding neural network, a localizer neural network, a multi-attention neural network, and a classifier neural network. In some cases, the disclosed systems train the multi-attribute contrastive classification neural network utilizing a multi-attribute, supervised-contrastive loss. In some embodiments, the disclosed systems generate negative attribute training labels for labeled digital images utilizing positive attribute labels that correspond to the labeled digital images.
-
Patent Agency Ranking
公开(公告)号：US20250022099A1
公开(公告)日：2025-01-16
申请号：US18351838
申请日：2023-07-13
Applicant: ADOBE INC.
Inventor： Yizhi Song , Zhifei Zhang , Zhe Lin , Scott Cohen , Brian Lynn Price , Jianming Zhang , Soo Ye Kim
Abstract: Systems and methods for image compositing are provided. An aspect of the systems and methods includes obtaining a first image and a second image, wherein the first image includes a target location and the second image includes a target element; encoding the second image using an image encoder to obtain an image embedding; generating a descriptive embedding based on the image embedding using an adapter network; and generating a composite image based on the descriptive embedding and the first image using an image generation model, wherein the composite image depicts the target element from the second image at the target location of the first image.
-
137.发明授权公开(公告)号：US12190484B2
公开(公告)日：2025-01-07
申请号：US17202019
申请日：2021-03-15
Applicant: Adobe Inc.
Inventor： Sohrab Amirghodsi , Lingzhi Zhang , Zhe Lin , Connelly Barnes , Elya Shechtman
IPC: G06T5/77 , G06N3/08 , G06T3/4053 , G06T7/11 , G06T7/50
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly generating modified digital images utilizing a guided inpainting approach that implements a patch match model informed by a deep visual guide. In particular, the disclosed systems can utilize a visual guide algorithm to automatically generate guidance maps to help identify replacement pixels for inpainting regions of digital images utilizing a patch match model. For example, the disclosed systems can generate guidance maps in the form of structure maps, depth maps, or segmentation maps that respectively indicate the structure, depth, or segmentation of different portions of digital images. Additionally, the disclosed systems can implement a patch match model to identify replacement pixels for filling regions of digital images according to the structure, depth, and/or segmentation of the digital images.
-
公开(公告)号：US12165295B2
公开(公告)日：2024-12-10
申请号：US17661985
申请日：2022-05-04
Applicant: Adobe Inc.
Inventor： Haitian Zheng , Zhe Lin , Jingwan Lu , Scott Cohen , Elya Shechtman , Connelly Barnes , Jianming Zhang , Ning Xu , Sohrab Amirghodsi
IPC: G06T5/77 , G06T3/4046 , G06V10/40
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that generate inpainted digital images utilizing a cascaded modulation inpainting neural network. For example, the disclosed systems utilize a cascaded modulation inpainting neural network that includes cascaded modulation decoder layers. For example, in one or more decoder layers, the disclosed systems start with global code modulation that captures the global-range image structures followed by an additional modulation that refines the global predictions. Accordingly, in one or more implementations, the image inpainting system provides a mechanism to correct distorted local details. Furthermore, in one or more implementations, the image inpainting system leverages fast Fourier convolutions block within different resolution layers of the encoder architecture to expand the receptive field of the encoder and to allow the network encoder to better capture global structure.
-
公开(公告)号：US20240404243A1
公开(公告)日：2024-12-05
申请号：US18328950
申请日：2023-06-05
Applicant: ADOBE INC.
Inventor： Handong Zhao , Yue Bai , Zhe Lin , Ajinkya Gorakhnath Kale , Jiuxiang Gu , Tong Yu , Sungchul Kim
IPC: G06V10/75 , G06F16/332 , G06V10/774
Abstract: Systems and methods for multimodal machine learning are provided. According to one aspect, a method for multimodal machine learning includes obtaining a prompt; encoding the prompt using a multimodal encoder to obtain a prompt embedding, wherein the encoding comprises generating a plurality of multi-head attention (MHA) outputs corresponding to a plurality of different scales, respectively, and combining the plurality of MHA outputs using a multi-scale aggregator; and generating a response to the prompt based on the prompt embedding.
-
140.发明授权公开(公告)号：US12159380B2
公开(公告)日：2024-12-03
申请号：US17664991
申请日：2022-05-25
Applicant: Adobe Inc.
Inventor： Connelly Barnes , Elya Shechtman , Sohrab Amirghodsi , Zhe Lin
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that implement an inpainting framework having computer-implemented machine learning models to generate high-resolution inpainting results. For instance, in one or more embodiments, the disclosed systems generate an inpainted digital image utilizing a deep inpainting neural network from a digital image having a replacement region. The disclosed systems further generate, utilizing a visual guide algorithm, at least one deep visual guide from the inpainted digital image. Using a patch match model and the at least one deep visual guide, the disclosed systems generate a plurality of modified digital images from the digital image by replacing the region of pixels of the digital image with replacement pixels. Additionally, the disclosed systems select, utilizing an inpainting curation model, a modified digital image from the plurality of modified digital images to provide to a client device.
-
-
-
-
-
-
-
-
-
Search Results
393
records
(took 0.027 seconds)
