<ul><li>Convolutional Neural Networks (CNNs) have been the backbone of computer vision, excelling in image-related tasks.</li><li>Vision Transformers (ViTs) challenge CNN dominance by using self-attention mechanisms instead of convolutions to process images.</li><li>ViTs outperform CNNs when pre-trained on large datasets, but struggle with limited data.</li><li>Efficient architectures are being researched to address the quadratic complexity in ViTs self-attention.</li></ul>

Comparing Vision Transformers (ViT) vs. Convolutional Neural Networks (CNNs): A Deep Dive

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