An end-to-end learnable tokenizer for Vision Transformers that enhances spatial and semantic learning by allowing retrofitting of pretrained models to use pixel-level tokens

This paper advocates for a unified framework for surrogate model training and evaluation to address methodological fragmentation, improve reproducibility, and enable cross-domain collaboration in scientific research.