Author: Leonardo Tanzi

Leonardo Tanzi
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http://www.marktechpost.com
Leonardo Tanzi is currently a Ph.D. Student at the Polytechnic University of Turin, Italy. His current research focuses on human-machine methodologies for smart support during complex interventions in the medical domain, using Deep Learning and Augmented Reality for 3D assistance.

Researchers At UC Berkeley Propose IntructPix2Pix: A Diffusion Model To Edit Images From Human-Written Instructions

In recent years, the possible applications of text-to-image models have increased enormously. However, image editing to human-written instruction is one subfield that still has...

Google Brain and Tel Aviv University Researchers Proposed A Text-To-Image Model Guided By Sketches

Large text-to-image diffusion models have been an innovative tool for creating and editing content because they make it possible to synthesize a variety of...

Google AI Extends Imagen To Imagen-Video, A Text-To-Video Cascaded Diffusion Model To Generate High-Quality Video With Strong Temporal Consistency And Deep Language Understanding

In recent years (or even months), we have seen tremendous growth in generative model research. In particular, text-to-image models such as DALL-E2, Imagen, or...

Latest Artificial Intelligence (AI) Research At Google Presents ‘Imagic,’ An Effective Technique Based On Diffusion Models To Edit Images With Text Prompts

Especially in the last few years, real-world photo editing with non-trivial semantic adjustments has been a fascinating challenge in image processing. In particular, being...

Latest Computer Vision Research Proposes SLaK (Sparse Large Kernel Network), a Pure Convolutional Neural Network (CNN) Architecture based on Dynamic Sparsity Equipped with an...

Since their introduction in the ImageNet competition with AlexNet, Convolutional Neural Networks (CNNs) have always been the most used architecture in vision. However, in...

Latest Computer Vision Research at Google and Boston University Proposes ‘DreamBooth,’ A Technique for Fine-Tuning a Text-to-Image Model with a very Limited Set of...

In recent years, text-to-image models have evolved at an exciting speed. The quality of the results given by models such as Open AI’s DALL-E2...

NVIDIA and Tel-Aviv University Researchers Propose a Computer Vision Method based on Textual Inversion to Insert New Concepts into Pre-Trained Text-to-Image Models

In the last few years, text-to-image has become one of the most studied topics in the Computer Vision world, resulting in models such as...

Researchers from Microsoft Asia and Peking University Proposed NUWA-Infinity, a Model to Generate High-Resolution, Arbitrarily-Sized Images and Videos

In recent years, the generation of images or videos from different types of inputs (text, visual, or multimodal) has gained increased popularity. In this...

Researchers From China Propose ‘LViT’, A Language-Vision Model To Leverage Text Medical Reports For Improved Segmentation

Among the many applications of Deep Learning in healthcare, segmentation is undoubtedly one of the most studied, given the broad range of possible advantages...

Researchers from China Propose DAT: a Deformable Vision Transformer to Compute Self-Attention in a Data-Aware Fashion

In recent years, the extension of transformers in the computer vision field has slowly made vision transformers (ViT) the state-of-the-art model for many topics,...

A New Technique to Train Diffusion Model in Latent Space Using Limited Computational Resources While Maintaining High-Resolution Quality

In recent years, image synthesis has experienced exponential growth in performance. The two main approaches to this task have been autoregressive transformers (ARs) and...

UC Berkeley And Adobe AI Researchers Propose BlobGAN, A New Unsupervised And Mid-Level Representation For Insane Scene Manipulation

Since the advent of computer vision, one of the fundamental questions of the research community has always been how to represent the incredible richness...

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