HARMONIZING COMPOSITE IMAGES UTILIZING A SEMANTIC-GUIDED TRANSFORMER NEURAL NETWORK

The present disclosure relates to systems, non-transitory computer-readable media, and methods that implement a multi-branch harmonization neural network architecture to harmonize composite images. For example, in one or more implementations, the semantic-guided transformer-based harmonization syste...

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Bibliographic Details
Main Authors Zhang, He, Jung, Hyun Joon
Format Patent
LanguageEnglish
Published 16.05.2024
Subjects
CALCULATING
COMPUTING
COUNTING
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
PHYSICS
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Summary:The present disclosure relates to systems, non-transitory computer-readable media, and methods that implement a multi-branch harmonization neural network architecture to harmonize composite images. For example, in one or more implementations, the semantic-guided transformer-based harmonization system uses a convolutional branch, a transformer branch, and a semantic branch to generate a harmonized composite image based on an input composite image and a corresponding segmentation mask. More particularly, the convolutional branch comprises a series of convolutional neural network layers followed by a style normalization layer to extract localized information from the input composite image. Further, the transformer branch comprises a series of transformer neural network layers to extract global information based on different resolutions of the input composite image. The semantic branch includes a visual neural network that generates semantic features that inform the harmonization of the composite images.
Bibliography:Application Number: US202218053027