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GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation

ICLR 2023 · Detection and segmentation › CNN features · approach: Plain and high-resolution ViTs · 7 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add GP Block in place of window attention (ablation)window-attention GPViT (L1) · ImageNet-1KTop-1 Acc: 78.1 -> 79.8Helped
add GP Block on top of LePE attention (ablation)LePE-attention GPViT (L1) · ImageNet-1KTop-1 Acc: 79.5 -> 80.5Helped
add GP Block on top of window attention (ablation)window-attention GPViT (L1) · ADE20K SegFormermIoU: 41.7 -> 45.5Helped
add GP Block on top of LePE attention (ablation)LePE-attention GPViT (L1) · ADE20K SegFormermIoU: 46.2 -> 46.9Helped
replace local attention with convolution propagation blocks (ablation)GPViT (window and LePE attention) · ImageNet-1KTop-1 Acc: 78.2 -> 75.8 (window); 79.5 -> 77.7 (LePE)Hurt
replace GP Block with global attention (ablation)GPViT (LePE attention) · ImageNet-1KTop-1 Acc: 80.4 vs 80.5 for GP Block at ~10x FLOPs (62G vs 34G ADE20K)Mixed
use shifted window attention instead of GP Block (ablation)window-attention GPViT · ADE20K SegFormermIoU: 41.7 -> 40.7Hurt
remove group propagation (grouping and ungrouping only) (ablation)GP Block · ImageNet-1KTop-1 Acc: 80.5 -> 79.8Hurt
use self-attention instead of MLPMixer for group propagation (ablation)GP Block · ImageNet-1KTop-1 Acc: 80.5 -> 80.7 at higher FLOPs (6.2G vs 5.8G)Helped
increase patch resolution (patch 16 to 8) with global self-attention (ablation)ViT-D216 · ImageNet-1KTop-1 Acc: 77.4 -> 79.2Helped
GPViT-L1 vs Swin-B backbone in Mask R-CNN 1xGPViT-L1 · COCO Mask R-CNN 1xAP^bb: 45.5 -> 48.1Helped
GPViT-L3 vs Swin-B on ADE20K UperNetGPViT-L3 · ADE20K UperNetmIoU: 49.7 -> 51.7Helped

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