公开(公告)号：US20220198717A1

公开(公告)日：2022-06-23

申请号：US17654529

申请日：2022-03-11

Applicant: Adobe Inc.

Inventor： Meet Patel ,  Mayur Hemani ,  Karanjeet Singh ,  Amit Gupta ,  Apoorva Gupta ,  Balaji Krishnamurthy

IPC: G06T9/00 ,  G06N3/08 ,  G06T7/00

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing deep learning to intelligently determine compression settings for compressing a digital image. For instance, the disclosed system utilizes a neural network to generate predicted perceptual quality values for compression settings on a compression quality scale. The disclosed system fits the predicted compression distortions to a perceptual distortion characteristic curve for interpolating predicted perceptual quality values across the compression settings on the compression quality scale. Additionally, the disclosed system then performs a search over the predicted perceptual quality values for the compression settings along the compression quality scale to select a compression setting based on a perceptual quality threshold. The disclosed system generates a compressed digital image according to compression parameters for the selected compression setting.

公开(公告)号：US11301506B2

公开(公告)日：2022-04-12

申请号：US15637829

申请日：2017-06-29

Applicant: Adobe Inc.

Inventor： Mayur Hemani ,  Balaji Krishnamurthy

IPC: G06F16/00 ,  G06F16/48 ,  G06F3/0482 ,  G06F16/36 ,  G06F16/332 ,  G06F16/33 ,  G06F16/55 ,  G06F16/56

Abstract: Automated digital asset tagging techniques and systems are described that support use of multiple vocabulary sets. In one example, a plurality of digital assets are obtained having first-vocabulary tags taken from a first-vocabulary set. Second-vocabulary tags taken from a second-vocabulary set are assigned to the plurality of digital assets through machine learning. A determination is made that at least one first-vocabulary tag includes a plurality of visual classes based on the assignment of at least one second-vocabulary tag. Digital assets are collected from the plurality of digital assets that correspond to one visual class of the plurality of visual classes. The model is generated using machine learning based on the collected digital assets.

公开(公告)号：US11030782B2

公开(公告)日：2021-06-08

申请号：US16679165

申请日：2019-11-09

Applicant: Adobe Inc.

Inventor： Kumar Ayush ,  Surgan Jandial ,  Abhijeet Kumar ,  Mayur Hemani ,  Balaji Krishnamurthy ,  Ayush Chopra

IPC: G06T11/40 ,  G06T3/00 ,  G06N3/08 ,  G06N3/04

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a virtual try-on digital image utilizing a unified neural network framework. For example, the disclosed systems can utilize a coarse-to-fine warping process to generate a warped version of a product digital image to fit a model digital image. In addition, the disclosed systems can utilize a texture transfer process to generate a corrected segmentation mask indicating portions of a model digital image to replace with a warped product digital image. The disclosed systems can further generate a virtual try-on digital image based on a warped product digital image, a model digital image, and a corrected segmentation mask. In some embodiments, the disclosed systems can train one or more neural networks to generate accurate outputs for various stages of generating a virtual try-on digital image.