公开(公告)号：US11676283B2

公开(公告)日：2023-06-13

申请号：US17660361

申请日：2022-04-22

Applicant: Adobe Inc.

Inventor： Zichuan Liu ,  Wentian Zhao ,  Shitong Wang ,  He Qin ,  Yumin Jia ,  Yeojin Kim ,  Xin Lu ,  Jen-Chan Chien

IPC: G06T7/11

CPC classification number: G06T7/11 ,  G06T2207/20084

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate refined segmentation masks for digital visual media items. For example, in one or more embodiments, the disclosed systems utilize a segmentation refinement neural network to generate an initial segmentation mask for a digital visual media item. The disclosed systems further utilize the segmentation refinement neural network to generate one or more refined segmentation masks based on uncertainly classified pixels identified from the initial segmentation mask. To illustrate, in some implementations, the disclosed systems utilize the segmentation refinement neural network to redetermine whether a set of uncertain pixels corresponds to one or more objects depicted in the digital visual media item based on low-level (e.g., local) feature values extracted from feature maps generated for the digital visual media item.

公开(公告)号：US11615308B2

公开(公告)日：2023-03-28

申请号：US17563901

申请日：2021-12-28

Applicant: Adobe Inc.

Inventor： Wentian Zhao ,  Seokhwan Kim ,  Ning Xu ,  Hailin Jin

IPC: G06K9/00 ,  G06N3/02 ,  G06F17/16 ,  G06N3/08 ,  G06V20/40 ,  G06V30/18 ,  G06V30/19 ,  G06V10/82 ,  G06V20/62 ,  G06V30/10

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for generating a response to a question received from a user during display or playback of a video segment by utilizing a query-response-neural network. The disclosed systems can extract a query vector from a question corresponding to the video segment using the query-response-neural network. The disclosed systems further generate context vectors representing both visual cues and transcript cues corresponding to the video segment using context encoders or other layers from the query-response-neural network. By utilizing additional layers from the query-response-neural network, the disclosed systems generate (i) a query-context vector based on the query vector and the context vectors, and (ii) candidate-response vectors representing candidate responses to the question from a domain-knowledge base or other source. To respond to a user's question, the disclosed systems further select a response from the candidate responses based on a comparison of the query-context vector and the candidate-response vectors.

公开(公告)号：US20220138913A1

公开(公告)日：2022-05-05

申请号：US17085491

申请日：2020-10-30

Applicant: Adobe Inc.

Inventor： Sheng-Wei Huang ,  Wentian Zhao ,  Kun Wan ,  Zichuan Liu ,  Xin Lu ,  Jen-Chan Jeff Chien

IPC: G06T5/00 ,  H04N5/232 ,  G06K9/62 ,  G06T7/73

Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for accurately and efficiently removing objects from digital images taken from a camera viewfinder stream. For example, the disclosed systems access digital images from a camera viewfinder stream in connection with an undesired moving object depicted in the digital images. The disclosed systems generate a temporal window of the digital images concatenated with binary masks indicating the undesired moving object in each digital image. The disclosed systems further utilizes a 3D to 2D generator as part of a 3D to 2D generative adversarial neural network in connection with the temporal window to generate a target digital image with the region associated with the undesired moving object in-painted. In at least one embodiment, the disclosed systems provide the target digital image to a camera viewfinder display to show a user how a future digital photograph will look without the undesired moving object.

公开(公告)号：US11244167B2

公开(公告)日：2022-02-08

申请号：US16784005

申请日：2020-02-06

Applicant: Adobe Inc.

Inventor： Wentian Zhao ,  Seokhwan Kim ,  Ning Xu ,  Hailin Jin

IPC: G06K9/00 ,  G06N3/02 ,  G06F17/16

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for generating a response to a question received from a user during display or playback of a video segment by utilizing a query-response-neural network. The disclosed systems can extract a query vector from a question corresponding to the video segment using the query-response-neural network. The disclosed systems further generate context vectors representing both visual cues and transcript cues corresponding to the video segment using context encoders or other layers from the query-response-neural network. By utilizing additional layers from the query-response-neural network, the disclosed systems generate (i) a query-context vector based on the query vector and the context vectors, and (ii) candidate-response vectors representing candidate responses to the question from a domain-knowledge base or other source. To respond to a user's question, the disclosed systems further select a response from the candidate responses based on a comparison of the query-context vector and the candidate-response vectors.

公开(公告)号：US20210248376A1

公开(公告)日：2021-08-12

申请号：US16784005

申请日：2020-02-06

Applicant: Adobe Inc.

Inventor： Wentian Zhao ,  Seokhwan Kim ,  Ning Xu ,  Hailin Jin

IPC: G06K9/00 ,  G06N3/02 ,  G06F17/16

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for generating a response to a question received from a user during display or playback of a video segment by utilizing a query-response-neural network. The disclosed systems can extract a query vector from a question corresponding to the video segment using the query-response-neural network. The disclosed systems further generate context vectors representing both visual cues and transcript cues corresponding to the video segment using context encoders or other layers from the query-response-neural network. By utilizing additional layers from the query-response-neural network, the disclosed systems generate (i) a query-context vector based on the query vector and the context vectors, and (ii) candidate-response vectors representing candidate responses to the question from a domain-knowledge base or other source. To respond to a user's question, the disclosed systems further select a response from the candidate responses based on a comparison of the query-context vector and the candidate-response vectors.