🤯 Did You Know (click to read)
Vision Transformers require larger datasets than CNNs to achieve optimal performance due to reduced inductive biases.
Vision Transformers (ViT) split images into patches, flatten them, and add positional encodings. Self-attention layers learn relationships between patches, capturing global context without convolutions. This enables high-accuracy classification and object detection while leveraging the same attention mechanisms developed for NLP.
💥 Impact (click to read)
Vision Transformers outperform traditional convolutional neural networks on large datasets, improving computer vision applications like autonomous vehicles and medical imaging.
Researchers and developers can leverage Transformers for multimodal AI systems, integrating vision and language understanding.
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