公开(公告)号：US20250054164A1

公开(公告)日：2025-02-13

申请号：US18232131

申请日：2023-08-09

Applicant: Adobe Inc.

Inventor： Silky SINGH ,  Shirpad DESHMUKH ,  Rishabh JAIN ,  Mayur HEMANI ,  Mausoom SARKAR ,  Balaji KRISHNAMURTHY

IPC: G06T7/215 ,  G06T7/162 ,  G06T7/194 ,  G06T7/246

Abstract: Various disclosed embodiments are directed to learning-free video object segmentation (VOS) of one or more video frames. In various instances, such VOS does not rely on human supervision or labeling and can be generalized or applied to any video “in the wild.” This is because training and fine-tuning are not required for VOS. Particular embodiments perform VOS based on feature similarity of different sections of a video frame and/or estimated motion similarity of different sections of the video frame, such as via a graph cut on a graph.

公开(公告)号：US20250005048A1

公开(公告)日：2025-01-02

申请号：US18345990

申请日：2023-06-30

Applicant: Adobe Inc.

Inventor： Abhinav JAVA ,  Surgan JANDIAL ,  Shripad DESHMUKH ,  Milan AGGARWAL ,  Mausoom SARKAR ,  Balaji KRISHNAMURTHY ,  Arneh JAIN

IPC: G06F16/332

Abstract: Embodiments are disclosed for one-shot document snippet search. A method of one-shot document snippet search may include obtaining a query snippet and a target document. A multi-modal snippet detection model combines first multi-modal features from the query snippet and second multi-modal features from the target document to create a feature volume. The multi-modal snippet detection model identifies one or more matching snippets from the target document based on the feature volume.

公开(公告)号：US20210042625A1

公开(公告)日：2021-02-11

申请号：US16534856

申请日：2019-08-07

Applicant: ADOBE INC.

Inventor： Ayush CHOPRA ,  Abhishek SINHA ,  Hiresh GUPTA ,  Mausoom SARKAR ,  Kumar AYUSH ,  Balaji KRISHNAMURTHY

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods and systems are provided for facilitating the creation and utilization of a transformation function system capable of providing network agnostic performance improvement. The transformation function system receives a representation from a task neural network. The representation can be input into a composite function neural network of the transformation function system. A learned composite function can be generated using the composite function neural network. The composite function can be specifically constructed for the task neural network based on the input representation. The learned composite function can be applied to a feature embedding of the task neural network to transform the feature embedding. Transforming the feature embedding can optimize the output of the task neural network.