LOOPE learns a patch ordering for positional embeddings in ViTs and introduces the Three Cell Experiment benchmark that shows 30-35% gaps in positional retention versus the usual 4-6%.
Going deeper with im- age transformers
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2025 2verdicts
UNVERDICTED 2representative citing papers
Primus and PrimusV2 are Transformer-centric models that match or exceed nnU-Net and top CNNs on nine 3D medical segmentation datasets by enforcing attention usage.
citing papers explorer
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LOOPE: Learnable Optimal Patch Order in Positional Embeddings for Vision Transformers
LOOPE learns a patch ordering for positional embeddings in ViTs and introduces the Three Cell Experiment benchmark that shows 30-35% gaps in positional retention versus the usual 4-6%.
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Primus: Enforcing Attention Usage for 3D Medical Image Segmentation
Primus and PrimusV2 are Transformer-centric models that match or exceed nnU-Net and top CNNs on nine 3D medical segmentation datasets by enforcing attention usage.