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%.
A survey on graph neural networks and graph transformers in computer vision: A task-oriented perspective
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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%.