Swin Transformer reaches 87.3% ImageNet accuracy and sets new records on COCO detection and ADE20K segmentation by replacing global self-attention with shifted-window local attention inside a hierarchical pyramid.
Rethinking semantic segmen- tation from a sequence-to-sequence perspective with trans- formers
4 Pith papers cite this work, alongside 198 external citations. Polarity classification is still indexing.
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Pith papers citing it
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representative citing papers
BEiT pre-trains vision transformers via masked image modeling on visual tokens and reaches 83.2% ImageNet top-1 accuracy for the base model and 86.3% for the large model using only ImageNet-1K data.
TransUNet is a hybrid CNN-Transformer architecture that outperforms prior U-Net and Transformer baselines on multi-organ and cardiac medical image segmentation tasks.
IViT applies quadratic programming to a pre-trained Vision Transformer with a multi-objective loss, achieving 93.80% accuracy on six skin disease datasets (0.21% below baseline) while reducing feature redundancy by 29.5% and producing clinically consistent activations.
citing papers explorer
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Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Swin Transformer reaches 87.3% ImageNet accuracy and sets new records on COCO detection and ADE20K segmentation by replacing global self-attention with shifted-window local attention inside a hierarchical pyramid.
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BEiT: BERT Pre-Training of Image Transformers
BEiT pre-trains vision transformers via masked image modeling on visual tokens and reaches 83.2% ImageNet top-1 accuracy for the base model and 86.3% for the large model using only ImageNet-1K data.
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TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
TransUNet is a hybrid CNN-Transformer architecture that outperforms prior U-Net and Transformer baselines on multi-organ and cardiac medical image segmentation tasks.
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IViT: A Novel Interpretable Visual Transformer for Skin Disease Detection
IViT applies quadratic programming to a pre-trained Vision Transformer with a multi-objective loss, achieving 93.80% accuracy on six skin disease datasets (0.21% below baseline) while reducing feature redundancy by 29.5% and producing clinically consistent activations.