Computer vision, a prominent branch of artificial intelligence, is centered around endowing computers with the ability to decipher and comprehend the visual world. It integrates a plethora of algorithms and advanced machine learning techniques, such as transformers, to scrutinize and make sense of visual data sourced from cameras and other imaging devices.
A mathematical model of computer vision delineates the fundamental tenets and procedures inherent in visual perception. The overarching aim is to emulate the intricate functionality of the human visual system, enabling computers to precisely identify, categorize objects, individuals, and scenes within images and videos.
Computer vision models manifest in a wide array of forms. Traditional ones include feature - based models, deep learning networks, and convolutional neural networks (CNNs). However, in recent years, models based on the transformer architecture have emerged as a revolutionary force in this field. Transformers, with their self - attention mechanism, are capable of capturing long - range dependencies in visual data, offering a novel perspective for visual analysis.
Currently, some of the cutting - edge vision models based on the transformer architecture include T-Rex2, Grounding DINO, DINO-X and so on.