Invention Application
- Patent Title: IDENTIFYING DIGITAL ATTRIBUTES FROM MULTIPLE ATTRIBUTE GROUPS UTILIZING A DEEP COGNITIVE ATTRIBUTION NEURAL NETWORK
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Application No.: US17806922Application Date: 2022-06-14
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Publication No.: US20220309093A1Publication Date: 2022-09-29
- Inventor: Ayush Chopra , Mausoom Sarkar , Jonas Dahl , Hiresh Gupta , Balaji Krishnamurthy , Abhishek Sinha
- 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: G06F16/535
- IPC: G06F16/535 ; G06K9/62 ; G06F17/15 ; G06N3/04 ; G06F16/55

Abstract:
The present disclosure relates to systems, methods, and non-transitory computer-readable media for generating tags for an object portrayed in a digital image based on predicted attributes of the object. For example, the disclosed systems can utilize interleaved neural network layers of alternating inception layers and dilated convolution layers to generate a localization feature vector. Based on the localization feature vector, the disclosed systems can generate attribute localization feature embeddings, for example, using some pooling layer such as a global average pooling layer. The disclosed systems can then apply the attribute localization feature embeddings to corresponding attribute group classifiers to generate tags based on predicted attributes. In particular, attribute group classifiers can predict attributes as associated with a query image (e.g., based on a scoring comparison with other potential attributes of an attribute group). Based on the generated tags, the disclosed systems can respond to tag queries and search queries.
Public/Granted literature
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