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How Vision Transformers Work?
The Paradigm Shift in Computer Vision
Over the past decade, Convolutional Neural Networks (CNNs) have dominated computer vision, excelling in tasks like image classification, segmentation, and object detection. However, their local receptive field limits the ability to model global dependencies effectively. Enter Vision Transformers (ViTs), a revolutionary model architecture inspired by the Transformer architecture in Natural Language Processing (NLP).
Introduced in the paper “An Image Is Worth 16x16 Words” by Dosovitskiy et al., Vision Transformers apply the self-attention mechanism to images, achieving state-of-the-art performance in image recognition tasks.
Why the Transition From CNNs to Vision Transformers?
The Dominance of CNNs
CNNs revolutionized computer vision due to their ability to:
- Capture local patterns like edges and textures.
- Exploit spatial hierarchies (low-level to high-level features).
- Leverage inductive biases such as translation invariance and locality.
However, these inductive biases can also be limiting:
- Global Context Modeling: CNNs rely on stacking layers to capture…
