Self-supervised pretraining for an iterative image size agnostic vision transformer

Nedyalko Prisadnikov; Danda Pani Paudel; Yuqian Fu; Luc Van Gool

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arXiv · 2604.20392

Self-supervised pretraining for an iterative image size agnostic vision transformer

Nedyalko Prisadnikov·Danda Pani Paudel·Yuqian Fu·Luc Van Gool

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Abstract

Vision Transformers (ViTs) dominate self-supervised learning (SSL). While they have proven highly effective for large-scale pretraining, they are computationally inefficient and scale poorly with image size. Consequently, foundational models like DINO are constrained to low-resolution processing. A recent foveal-inspired transformer achieves resolution agnosticism by iteratively processing a fixed-size context of multi-zoom patches. This model demonstrated promising results via supervised learning, utilizing a sequential, recurrent-like process without backpropagation through time. To unlock its potential as a foundational backbone, we introduce a novel sequential-to-global SSL framework based on DINO's self-distillation objective. Supported by an efficient integral-image patch extraction method, our approach enables large-scale pretraining for image-size agnostic vision encoders. We achieve competitive performance on ImageNet-1K and downstream classification tasks, maintaining a constant computational budget regardless of input resolution.

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Nedyalko Prisadnikov, Danda Pani Paudel, Yuqian Fu, Luc Van Gool. 2026-04-22. Self-supervised pretraining for an iterative image size agnostic vision transformer. https://arxiv.org/abs/2604.20392

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