Invention Grant
- Patent Title: Extracting attributes from arbitrary digital images utilizing a multi-attribute contrastive classification neural network
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Application No.: US17332734Application Date: 2021-05-27
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Publication No.: US12136250B2Publication Date: 2024-11-05
- Inventor: Khoi Pham , Kushal Kafle , Zhe Lin , Zhihong Ding , Scott Cohen , Quan Tran
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Agency: Keller Preece PLLC
- Main IPC: G06V10/75
- IPC: G06V10/75 ; G06F18/214 ; G06F18/25 ; G06N3/08

Abstract:
This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that extract multiple attributes from an object portrayed in a digital image utilizing a multi-attribute contrastive classification neural network. For example, the disclosed systems utilize a multi-attribute contrastive classification neural network that includes an embedding neural network, a localizer neural network, a multi-attention neural network, and a classifier neural network. In some cases, the disclosed systems train the multi-attribute contrastive classification neural network utilizing a multi-attribute, supervised-contrastive loss. In some embodiments, the disclosed systems generate negative attribute training labels for labeled digital images utilizing positive attribute labels that correspond to the labeled digital images.
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