Invention Grant
- Patent Title: Center-biased machine learning techniques to determine saliency in digital images
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Application No.: US16507300Application Date: 2019-07-10
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Publication No.: US11663463B2Publication Date: 2023-05-30
- Inventor: Kumar Ayush , Atishay Jain
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Agency: Kilpatrick Townsend & Stockton LLP
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N3/082 ; G06V30/262 ; G06F18/213 ; G06V10/46 ; G06V10/82 ; G06V10/44

Abstract:
A location-sensitive saliency prediction neural network generates location-sensitive saliency data for an image. The location-sensitive saliency prediction neural network includes, at least, a filter module, an inception module, and a location-bias module. The filter module extracts visual features at multiple contextual levels, and generates a feature map of the image. The inception module generates a multi-scale semantic structure, based on multiple scales of semantic content depicted in the image. In some cases, the inception block performs parallel analysis of the feature map, such as by parallel multiple layers, to determine the multiple scales of semantic content. The location-bias module generates a location-sensitive saliency map of location-dependent context of the image based on the multi-scale semantic structure and on a bias map. In some cases, the bias map indicates location-specific weights for one or more regions of the image.
Public/Granted literature
- US20210012201A1 CENTER-BIASED MACHINE LEARNING TECHNIQUES TO DETERMINE SALIENCY IN DIGITAL IMAGES Public/Granted day:2021-01-14
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