A transformer-enabled model that takes in images, detects characters, and predicts a sequence of characters as a proxy for detecting the words. Trained on OCRTextExtraction Dataset From Kaggle with ...

The self-attention mechanism is a key component of Transformers. It allows the model to weigh the importance of different words in the input sequence when making predictions ... components into a full ...

Inspired by the recent success of transformers for Natural Language Processing (NLP) in long-range sequence learning, we reformulate the task of volumetric (3D) medical image segmentation as a ...

We introduce an efficient distributed sequence parallel approach for training transformer-based deep learning image segmentation models. The neural network models are comprised of a combination of a ...

Addressing these gaps, we propose SuTraN, a novel transformer architecture for PPM suffix prediction. SuTraN avoids iterative prediction loops and utilizes all available data, including event features ...