Accenture Global Solutions Limited

Peter Royer Smith, Jr

Sukryool Kang

Anupam Anurag Tripathi

Sujeong Cha

Surya Raghavendra Vadlamani

Anwitha Paruchuri

Daniel Garrison

Yuan He

Bo Zhang

Andrew Francis Hickl

Neha Wadhwa Dang

Mohamed Suhail

Jatinder Singh

Described herein are systems, methods, devices, and other techniques for comprehensive and automated evaluation of digital images generated from artificial intelligence (AI) models in order to promote accurate representations of real-world content. Prompts are received at the system that are then passed to both a search engine and a generative AI model. Synthesized digital images are obtained from the generative AI model. The top-matching image from the search engine is used as a verification of the ground truth of the synthesized digital images. A realism score is generated for each synthesized digital image that characterizes the accuracy of the synthesized digital image with reference to the verification image. The realism score can be used to assist and expedite the image selection process, as well as serve as input to fine-tune the performance of generative models.

System and method for extracting object information from digital images to evaluate for realism

Methods, systems, and apparatus are provided for generating an image. A personalized text prompt is generated by processing an input embedding using a transformer model followed by a first fully connected neural network. The input embedding comprises a multi-dimensional embedding vector associated with a user profile and a plurality of user items. A scored label set is generated identifying a user's preferences by processing a set of attributes for the plurality of user items using a second fully connected neural network. The image is generated by processing the personalized text prompt and the scored label set using a diffusion model.

Hyper-personalized prompt based content generation

Growing community of inventors

Sharon, CT, United States of America

Peter Royer Smith, Jr