The autoencoder captures TTE patterns and transforms them into CMR-like representations, enhanced by the vision transformer's attention mechanisms. Evaluation through quantitative and qualitative ...

In this work, we propose a Generative Convolutional Vision Transformer (GenConViT) for deepfake video detection. Our model combines ConvNeXt and Swin Transformer models for feature extraction, and it ...

This repository presents a novel hybrid model combining Convolutional Variational Auto-Encoder (CVAE) and Vision Transformer (ViT) for early Alzheimer's Disease detection. The model demonstrates 96% ...

GigaPath’s two-stage curriculum learning involves pretraining at the tile level with DINOv2 and pre-training at the slide level using masked autoencoder and LongNet. The DINOv2 self-supervision method ...

Vision Transformers, on the other hand, analyze an image more holistically, understanding relationships between different regions through an attention mechanism. A great analogy, as noted in Quanta ...

They follow a masked autoencoder (MAE) strategy during pretraining, ... Segmentation, and Depth Estimation with Vision Transformers ” Pingback: AI Progress Daily Report-08/28 – GoodAI. dsgsg323hi 2024 ...