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Transformers have demonstrated excellent capabilities and they overcome challenges such NLP, Text-To-Image Generation or Image Completion with large datasets, great model size and enough compute. Talking about transformers nowadays is as casual as talking about CNNs, MLPs or Linear Regressions. Why not take a glance through this state-of-the-art architecture? In this post, we’ll introduce the Sequence-to-Sequence (Seq2Seq) paradigm, explore the attention mechanism, and provide a detailed, step-by-step explanation of the components that make up transformer architectures....

Traditional computer vision techniques involve methods and algorithms that do not rely on deep learning or neural networks. Instead, these approaches are not data-driven and they use classical approaches to process and analyze images. So, in this post, we’ll explore three thresholding techniques! Thresholding When the task is to distinguish the background from the foreground, thresholding provides a straightforward solution. We will use this image as an example. This technique segments an image by assigning one value (typically white) to all pixels above a specified threshold and another value (usually black) to the remaining pixels....