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公开(公告)号：US11568544B2
公开(公告)日：2023-01-31
申请号：US17483280
申请日：2021-09-23
Applicant: Adobe Inc.
Inventor： Zhe Lin , Jianming Zhang , He Zhang , Federico Perazzi
Abstract: The present disclosure relates to utilizing a neural network having a two-stream encoder architecture to accurately generate composite digital images that realistically portray a foreground object from one digital image against a scene from another digital image. For example, the disclosed systems can utilize a foreground encoder of the neural network to identify features from a foreground image and further utilize a background encoder to identify features from a background image. The disclosed systems can then utilize a decoder to fuse the features together and generate a composite digital image. The disclosed systems can train the neural network utilizing an easy-to-hard data augmentation scheme implemented via self-teaching. The disclosed systems can further incorporate the neural network within an end-to-end framework for automation of the image composition process.
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公开(公告)号：US20230024955A1
公开(公告)日：2023-01-26
申请号：US17957639
申请日：2022-09-30
Applicant: Adobe Inc.
Inventor： Akhilesh Kumar , Zhe Lin , William Lawrence Marino
Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for detecting and classifying an exposure defect in an image using neural networks trained via a limited amount of labeled training images. An image may be applied to a first neural network to determine whether the images includes an exposure defect. Detected defective image may be applied to a second neural network to determine an exposure defect classification for the image. The exposure defect classification can includes severe underexposure, medium underexposure, mild underexposure, mild overexposure, medium overexposure, severe overexposure, and/or the like. The image may be presented to a user along with the exposure defect classification.
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公开(公告)号：US11544831B2
公开(公告)日：2023-01-03
申请号：US16984992
申请日：2020-08-04
Applicant: Adobe Inc.
Inventor： Yilin Wang , Zhe Lin , Zhaowen Wang , Xin Lu , Xiaohui Shen , Chih-Yao Hsieh
Abstract: The present disclosure relates to training and utilizing an image exposure transformation network to generate a long-exposure image from a single short-exposure image (e.g., still image). In various embodiments, the image exposure transformation network is trained using adversarial learning, long-exposure ground truth images, and a multi-term loss function. In some embodiments, the image exposure transformation network includes an optical flow prediction network and/or an appearance guided attention network. Trained embodiments of the image exposure transformation network generate realistic long-exposure images from single short-exposure images without additional information.
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公开(公告)号：US20220391611A1
公开(公告)日：2022-12-08
申请号：US17341778
申请日：2021-06-08
Applicant: ADOBE INC.
Inventor： RATHEESH KALAROT , Siavash Khodadadeh , Baldo Faieta , Shabnam Ghadar , Saeid Motiian , Wei-An Lin , Zhe Lin
Abstract: Systems and methods for image processing are described. One or more embodiments of the present disclosure identify a latent vector representing an image of a face, identify a target attribute vector representing a target attribute for the image, generate a modified latent vector using a mapping network that converts the latent vector and the target attribute vector into a hidden representation having fewer dimensions than the latent vector, wherein the modified latent vector is generated based on the hidden representation, and generate a modified image based on the modified latent vector, wherein the modified image represents the face with the target attribute.
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公开(公告)号：US20220383037A1
公开(公告)日：2022-12-01
申请号：US17332734
申请日：2021-05-27
Applicant: Adobe Inc.
Inventor： Khoi Pham , Kushal Kafle , Zhe Lin , Zhihong Ding , Scott Cohen , Quan Tran
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.
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Patent Agency Ranking
公开(公告)号：US20220327657A1
公开(公告)日：2022-10-13
申请号：US17220543
申请日：2021-04-01
Applicant: Adobe Inc.
Inventor： Haitian Zheng , Zhe Lin , Jingwan Lu , Scott Cohen , Jianming Zhang , Ning Xu
Abstract: This disclosure describes one or more implementations of a digital image semantic layout manipulation system that generates refined digital images resembling the style of one or more input images while following the structure of an edited semantic layout. For example, in various implementations, the digital image semantic layout manipulation system builds and utilizes a sparse attention warped image neural network to generate high-resolution warped images and a digital image layout neural network to enhance and refine the high-resolution warped digital image into a realistic and accurate refined digital image.
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公开(公告)号：US20220292654A1
公开(公告)日：2022-09-15
申请号：US17200338
申请日：2021-03-12
Applicant: Adobe Inc.
Inventor： He Zhang , Yifan Jiang , Yilin Wang , Jianming Zhang , Kalyan Sunkavalli , Sarah Kong , Su Chen , Sohrab Amirghodsi , Zhe Lin
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly generating harmonized digital images utilizing a self-supervised image harmonization neural network. In particular, the disclosed systems can implement, and learn parameters for, a self-supervised image harmonization neural network to extract content from one digital image (disentangled from its appearance) and appearance from another from another digital image (disentangled from its content). For example, the disclosed systems can utilize a dual data augmentation method to generate diverse triplets for parameter learning (including input digital images, reference digital images, and pseudo ground truth digital images), via cropping a digital image with perturbations using three-dimensional color lookup tables (“LUTs”). Additionally, the disclosed systems can utilize the self-supervised image harmonization neural network to generate harmonized digital images that depict content from one digital image having the appearance of another digital image.
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公开(公告)号：US20220284321A1
公开(公告)日：2022-09-08
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
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.
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公开(公告)号：US11367199B2
公开(公告)日：2022-06-21
申请号：US16900483
申请日：2020-06-12
Applicant: ADOBE INC.
Inventor： Lu Zhang , Jianming Zhang , Zhe Lin , Radomir Mech
Abstract: Systems and methods provide editing operations in a smart editing system that may generate a focal point within a mask of an object for each frame of a video segment and perform editing effects on the frames of the video segment to quickly provide users with natural video editing effects. An eye-gaze network may produce a hotspot map of predicted focal points in a video frame. These predicted focal points may then be used by a gaze-to-mask network to determine objects in the image and generate an object mask for each of the detected objects. This process may then be repeated to effectively track the trajectory of objects and object focal points in videos. Based on the determined trajectory of an object in a video clip and editing parameters, the editing engine may produce editing effects relative to an object for the video clip.
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公开(公告)号：US11354906B2
公开(公告)日：2022-06-07
申请号：US16846544
申请日：2020-04-13
Applicant: Adobe Inc.
Inventor： Federico Perazzi , Zhe Lin , Ping Hu , Oliver Wang , Fabian David Caba Heilbron
Abstract: A Video Semantic Segmentation System (VSSS) is disclosed that performs accurate and fast semantic segmentation of videos using a set of temporally distributed neural networks. The VSSS receives as input a video signal comprising a contiguous sequence of temporally-related video frames. The VSSS extracts features from the video frames in the contiguous sequence and based upon the extracted features, selects, from a set of labels, a label to be associated with each pixel of each video frame in the video signal. In certain embodiments, a set of multiple neural networks are used to extract the features to be used for video segmentation and the extraction of features is distributed among the multiple neural networks in the set. A strong feature representation representing the entirety of the features is produced for each video frame in the sequence of video frames by aggregating the output features extracted by the multiple neural networks.
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