公开(公告)号：US20230316379A1

公开(公告)日：2023-10-05

申请号：US18186528

申请日：2023-03-20

Applicant: Adobe Inc.

Inventor： Kumar AYUSH ,  Ayush Chopra ,  Patel U. Govind ,  Balaji Krishnamurthy ,  Anirudh Singhal

IPC: G06Q30/0601 ,  G06N3/088 ,  G06F18/214 ,  G06F18/21 ,  G06N3/045 ,  G06V10/764 ,  G06V10/82 ,  G06V10/44 ,  G06V20/00

CPC classification number: G06Q30/0631 ,  G06F18/214 ,  G06F18/2193 ,  G06N3/045 ,  G06N3/088 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  G06V20/00

Abstract: Systems, methods, and computer storage media are disclosed for predicting visual compatibility between a bundle of catalog items (e.g., a partial outfit) and a candidate catalog item to add to the bundle. Visual compatibility prediction may be jointly conditioned on item type, context, and style by determining a first compatibility score jointly conditioned on type (e.g., category) and context, determining a second compatibility score conditioned on outfit style, and combining the first and second compatibility scores into a unified visual compatibility score. A unified visual compatibility score may be determined for each of a plurality of candidate items, and the candidate item with the highest unified visual compatibility score may be selected to add to the bundle (e.g., fill the in blank for the partial outfit).

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.

公开(公告)号：US20210342701A1

公开(公告)日：2021-11-04

申请号：US16865572

申请日：2020-05-04

Applicant: ADOBE INC.

Inventor： Kumar AYUSH ,  Ayush CHOPRA ,  Patel Utkarsh GOVIND ,  Balaji KRISHNAMURTHY ,  Anirudh SINGHAL

IPC: G06N3/08 ,  G06N3/04 ,  G06Q30/06 ,  G06K9/62

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for predicting visual compatibility between a bundle of catalog items (e.g., a partial outfit) and a candidate catalog item to add to the bundle. Visual compatibility prediction may be jointly conditioned on item type, context, and style by determining a first compatibility score jointly conditioned on type (e.g., category) and context, determining a second compatibility score conditioned on outfit style, and combining the first and second compatibility scores into a unified visual compatibility score. A unified visual compatibility score may be determined for each of a plurality of candidate items, and the candidate item with the highest unified visual compatibility score may be selected to add to the bundle (e.g., fill the in blank for the partial outfit).