Vision Transformers (ViTs) outperform Convolutional Neural Networks (CNNs) in image classification due to factors like scalability and the ability to learn richer and more complex features with large datasets.
ViTs require more data or regularization to train effectively initially but demonstrate superior performance when pre-trained on massive image corpora compared to CNNs, achieving better efficiency at scale.
ViTs have been successful in various computer vision tasks beyond simple classification, such as object detection and image segmentation, where they have reached state-of-the-art results by capturing global context and long-range dependencies.
ViTs excel in fine-grained vision problems by focusing on subtle image details, making them valuable for tasks like fine-grained classification, biodiversity image recognition, and attribute classification, establishing themselves as a powerful approach in computer vision.