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arXiv · 2511.02652

Differentiable Hierarchical Visual Tokenization

Marius Aasan·Martine Hjelkrem-Tan·Nico Catalano·Changkyu Choi·Adín Ramírez Rivera
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Abstract

Vision Transformers rely on fixed patch tokens that ignore the spatial and semantic structure of images. In this work, we introduce an end-to-end differentiable tokenizer that adapts to image content with pixel-level granularity while remaining backward-compatible with existing architectures for retrofitting pretrained models. Our method uses hierarchical model selection with information criteria to provide competitive performance in both image-level classification and dense-prediction tasks, and even supports out-of-the-box raster-to-vector conversion.

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Marius Aasan, Martine Hjelkrem-Tan, Nico Catalano, Changkyu Choi, Adín Ramírez Rivera. 2025-11-04. Differentiable Hierarchical Visual Tokenization. https://arxiv.org/abs/2511.02652

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