In the last decade, convolutional neural networks (CNNs) have been the go-to architecture in computer vision, owing to their powerful capability in learning representations from images/videos.

Video clips from N2010 (Nakano et al., 2010) and CW2019 (Costela and Woods, 2019) were presented to ViTs. The gaze positions of each self-attention head in the class token ([CLS]) — identified as peak ...

Vision Transformers, or ViTs, are a groundbreaking learning model designed for tasks in computer vision, particularly image recognition. Unlike CNNs, which use convolutions for image processing, ViTs ...

Transformers, first proposed in a Google research paper in 2017, were initially designed for natural language processing (NLP) tasks. Recently, researchers applied transformers to vision applications ...

GenAI isn’t magic — it’s transformers using attention to understand context at scale. Knowing how they work will help CIOs ...

Vision transformers (ViTs) are powerful artificial intelligence (AI) technologies that can identify or categorize objects in images -- however, there are significant challenges related to both ...