The increasing convergence of computer vision and NLP
For decades, convolutional neural networks (CNNs) dominated computer vision. The inductive biases of convolutions—local connectivity, translation equivariance, and weight sharing—made them naturally suited for image processing.
Then came Vision Transformer (ViT).
The Vision Transformer Breakthrough
ViT, introduced by Google in 2020, showed that a pure Transformer architecture could achieve state-of-the-art results on image classification when trained on sufficient data. The key insight was simple: split an image into patches, treat each patch as a token, and apply standard Transformer layers.
This approach challenged the assumption that vision-specific inductive biases were necessary for good performance. With enough data and compute, Transformers could learn these patterns.
Why Transformers for Vision?
Global receptive field: Unlike CNNs, which build up receptive fields gradually through stacking layers, Transformers can attend to the entire image from the first layer.
Flexibility: The same architecture works across different modalities, enabling multimodal models that process text and images together.
Scalability: Transformers scale predictably with more data and compute.
Challenges and Solutions
Data efficiency: ViT initially required massive datasets. DeiT (Data-efficient Image Transformer) introduced training techniques that allowed competitive performance with ImageNet-scale data.
Computational cost: The quadratic complexity of attention is particularly problematic for high-resolution images. Swin Transformer addressed this with hierarchical feature maps and shifted window attention.
The Future
The convergence of NLP and CV architectures opens exciting possibilities for unified models that understand both language and vision. Foundation models like CLIP and DALL-E demonstrate the power of this approach.