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🤯 Did You Know (click to read)
Vision Transformers outperform CNNs in large datasets by leveraging attention for global context representation.
Vision Transformers (ViT) split images into patches, encode them, and process them through Transformer layers. Self-attention allows the model to capture spatial relationships across patches without convolution, improving representation of global image features.
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💥 Impact (click to read)
Vision Transformers achieve state-of-the-art performance on benchmarks like ImageNet, enabling new applications in autonomous vehicles, medical imaging, and surveillance.
For computer vision researchers, understanding Transformer adaptations enables cross-domain innovation and multimodal model development.
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