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Researchers from Peking University and Microsoft Introduce COLE: An Effective Hierarchical Generation Framework that can Convert a Simple Intention Prompt into a High-Quality Graphic Design

Natural picture production is now on par with professional photography, thanks to a notable recent improvement in quality. This advancement is attributable to creating technologies like DALL·E3, SDXL, and Imagen. Key elements driving these developments are using the potent Large Language Model (LLM) as a text encoder, scaling up training datasets, increasing model complexity, better…

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LLM and GNN: How to Improve Reasoning of Both AI Systems on Graph Data | by Anthony Alcaraz | Dec, 2023

Graph neural networks (GNNs) and large language models (LLMs) have emerged as two major branches of artificial intelligence, achieving immense success in learning from graph-structured and natural language data respectively. As graph-structured and natural language data become increasingly interconnected in real-world applications, there is a growing need for artificial intelligence systems that can perform multi-modal…

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What is Account Reconciliation?

You’ve likely heard the phrase, “measure twice, cut once.” Reconciling your balance sheet follows the same logic, but in reverse – spend once, check twice. Double-entry bookkeeping is built on a foundation of checks and balances, requiring the assets side to match the liabilities and shareholder’s equity side. Account reconciliation is one of many methods…

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Meet SceneTex: A Novel AI Method for High-Quality, Style-Consistent Texture Generation in Indoor Scenes

High-quality 3D content synthesis is a crucial yet challenging problem for many applications, such as autonomous driving, robotic simulation, gaming, filmmaking, and future VR/AR situations. The topic of 3D geometry generation has seen a surge in research interest from the computer vision and graphics community due to the availability of more and more 3D content…

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Breaking the Boundaries in 3D Scene Representation: How a New AI Technique is Changing the Game with Faster, More Efficient Rendering and Reduced Storage Demands

NeRF represents scenes as continuous 3D volumes. Instead of discrete 3D meshes or point clouds, it defines a function that calculates color and density values for any 3D point within the scene. By training the neural network on multiple scene images captured from different viewpoints, NeRF learns to generate consistent and accurate representations that align…

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