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State of transformers in Computer Vision

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Deep Kayal

Transformers have rapidly come up as a challenger network architecture to traditional convnets in computer vision. Here is a quick landscape analysis of the state of transformers in vision, as of 2021.

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Transformers in Vision Deep Kayal
Transformers are getting popular in vision after taking over NLP NLP Vision
Ingredient 1: Multi-head self-attention
Ingredient 2: Transformer architecture
Ingredient 3: Self-supervision NLP
Ingredient 3: Self-supervision Vision
Self-attention also exists in image processing
...with efficient variants
Transformers in Vision Self-attention Single-head Multi-head Non-local Criss-cross Single-scale Multi-scale +Convolutions Self-supervised ViT T-in-T LV-ViT XCit PViT* ** SegFormer* CvT** CLIP CoAtNet DINO
ViT: Vision Transformer Recipe for success: - Large deep model - Pretrained on large image database
T-in-T: Transformer in Transformer Recipe for success: Computing attention at two levels
LV-ViT: Token Labeling Transformers Recipe for success: Auxiliary loss based on image patches
XCiT: Cross-covariance Transformers Recipe for success: - Efficient self-attention - Processing of high-resolution images
PViT: Pyramid ViT* ** Recipe for success: - Progressive shrinking to capture different scales - Spatial reduction attention
SegFormer: Segmentation Transformer* Recipe for success: - Progressive shrinking to capture different scales - Overlapping windows
CvT: Convolutional Vision Transformers** Recipe for success: Convolution based projection to capture the spatial structure and low-level details
CoAtNet: Marrying Convolutions and Attention Recipe for success: Fusing the useful properties of convolution and attention
DINO: Self distillation without labels Recipe for success: Self supervised pre training
CLIP: Multi modal self supervision Recipe for success: Fusing text and images in pre training
Big models with big challenges Cons - Need large amounts of data - Lack of inductive bias, as in convolutions - High compute costs - Because of large amounts of data - Need for specific hardware - No Interpretability - Multi head attention is hard to interpret Pros - High “capacity” to learn general features - Loose argument: self attention is more global than convolutions - Ideally suited transfer learning paradigm - Train once, use always

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State of transformers in Computer Vision

Editor's Notes

  1. Why does it capture better features?