Invention Application
- Patent Title: SEGMENTING OBJECTS USING SCALE-DIVERSE SEGMENTATION NEURAL NETWORKS
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Application No.: US17655493Application Date: 2022-03-18
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Publication No.: US20220207745A1Publication Date: 2022-06-30
- Inventor: Scott Cohen , Long Mai , Jun Hao Liew , Brian Price
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Main IPC: G06T7/12
- IPC: G06T7/12 ; G06T7/194 ; G06T3/40 ; G06V10/25

Abstract:
The present disclosure relates to systems, non-transitory computer-readable media, and methods for training and utilizing scale-diverse segmentation neural networks to analyze digital images at different scales and identify different target objects portrayed in the digital images. For example, in one or more embodiments, the disclosed systems analyze a digital image and corresponding user indicators (e.g., foreground indicators, background indicators, edge indicators, boundary region indicators, and/or voice indicators) at different scales utilizing a scale-diverse segmentation neural network. In particular, the disclosed systems can utilize the scale-diverse segmentation neural network to generate a plurality of semantically meaningful object segmentation outputs. Furthermore, the disclosed systems can provide the plurality of object segmentation outputs for display and selection to improve the efficiency and accuracy of identifying target objects and modifying the digital image.
Public/Granted literature
- US12254633B2 Segmenting objects using scale-diverse segmentation neural networks Public/Granted day:2025-03-18
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