ViT

Vision Transformers and Object Recognition Vision transformers bring a fresh view to how machines recognize objects in images. Born from models designed for language, they use self-attention to relate all parts of an image to each other. When trained on large data, these models can match or exceed traditional convolutional approaches on many recognition tasks. The shift matters because it emphasizes global context, not just local patterns, which helps in scenes with occlusion, clutter, or unusual viewpoints. ...