公开(公告)号：US20250147973A1

公开(公告)日：2025-05-08

申请号：US18504256

申请日：2023-11-08

Applicant: ADOBE INC.

Inventor： Tong Yu ,  Xiang Chen ,  Victor Soares Bursztyn ,  Uttaran Bhattacharya ,  Eunyee Koh ,  Saayan Mitra ,  Alexandru Ionut Hodorogea ,  Kenneth Russell ,  Saurabh Tripathy ,  Manas Garg

IPC: G06F16/2457 ,  G06F16/93 ,  G06N20/20

Abstract: A method, apparatus, non-transitory computer readable medium, and system for document retrieval include obtaining a query and a document. A prompt generator generates a prompt for a reasoning model based on the query and the document. The reasoning model generates a reasoning result based on the prompt. In some cases, the reasoning result indicates that the document answers the query. A machine learning model provides the document in response to the query based on the reasoning result.

公开(公告)号：US12238451B2

公开(公告)日：2025-02-25

申请号：US18055301

申请日：2022-11-14

Applicant: Adobe Inc.

Inventor： Uttaran Bhattacharya ,  Gang Wu ,  Viswanathan Swaminathan ,  Stefano Petrangeli

IPC: H04N7/00 ,  G06T11/60

Abstract: Embodiments are disclosed for predicting, using neural networks, editing operations for application to a video sequence based on processing conversational messages by a video editing system. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input including a video sequence and text sentences, the text sentences describing a modification to the video sequence, mapping, by a first neural network content of the text sentences describing the modification to the video sequence to a candidate editing operation, processing, by a second neural network, the video sequence to predict parameter values for the candidate editing operation, and generating a modified video sequence by applying the candidate editing operation with the predicted parameter values to the video sequence.

公开(公告)号：US11574477B2

公开(公告)日：2023-02-07

申请号：US17194755

申请日：2021-03-08

Applicant: Adobe Inc.

Inventor： Gang Wu ,  Viswanathan Swaminathan ,  Uttaran Bhattacharya ,  Stefano Petrangeli

IPC: G06V20/40 ,  G06N3/08 ,  G06T7/73 ,  G06V40/16

Abstract: In implementations for highlight video generated with adaptable multimodal customization, a multimodal detection system tracks activities based on poses and faces of persons depicted in video clips of video content. The system determines a pose highlight score and a face highlight score for each of the video clips that depict at least one person, the highlight scores representing a relative level of the interest in an activity depicted in a video clip. The system also determines pose-based emotion features for each of the video clips. The system can detect actions based on the activities of the persons depicted in the video clips, and detect emotions exhibited by the persons depicted in the video clips. The system can receive input selections of actions and emotions, and filter the video clips based on the selected actions and emotions. The system can then generate a highlight video of ranked and filtered video clips.

公开(公告)号：US20220284220A1

公开(公告)日：2022-09-08

申请号：US17194755

申请日：2021-03-08

Applicant: Adobe Inc.

Inventor： Gang Wu ,  Viswanathan Swaminathan ,  Uttaran Bhattacharya ,  Stefano Petrangeli

IPC: G06K9/00 ,  G06T7/73 ,  G06N3/08

Abstract: In implementations for highlight video generated with adaptable multimodal customization, a multimodal detection system tracks activities based on poses and faces of persons depicted in video clips of video content. The system determines a pose highlight score and a face highlight score for each of the video clips that depict at least one person, the highlight scores representing a relative level of the interest in an activity depicted in a video clip. The system also determines pose-based emotion features for each of the video clips. The system can detect actions based on the activities of the persons depicted in the video clips, and detect emotions exhibited by the persons depicted in the video clips. The system can receive input selections of actions and emotions, and filter the video clips based on the selected actions and emotions. The system can then generate a highlight video of ranked and filtered video clips.