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arXiv · 2602.08071

ViT-5: Vision Transformers for The Mid-2020s

Feng Wang·Sucheng Ren·Tiezheng Zhang·Predrag Neskovic·Anand Bhattad·Cihang Xie·Alan Yuille
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Abstract

This work presents a systematic investigation into modernizing Vision Transformer backbones by leveraging architectural advancements from the past five years. While preserving the canonical Attention-FFN structure, we conduct a component-wise refinement involving normalization, activation functions, positional encoding, gating mechanisms, and learnable tokens. These updates form a new generation of Vision Transformers, which we call ViT-5. Extensive experiments demonstrate that ViT-5 consistently outperforms state-of-the-art plain Vision Transformers across both understanding and generation benchmarks. On ImageNet-1k classification, ViT-5-Base reaches 84.2\% top-1 accuracy under comparable compute, exceeding DeiT-III-Base at 83.8\%. ViT-5 also serves as a stronger backbone for generative modeling: when plugged into an SiT diffusion framework, it achieves 1.84 FID versus 2.06 with a vanilla ViT backbone. Beyond headline metrics, ViT-5 exhibits improved representation learning and favorable spatial reasoning behavior, and transfers reliably across tasks. With a design aligned with contemporary foundation-model practices, ViT-5 offers a simple drop-in upgrade over vanilla ViT for mid-2020s vision backbones.

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Feng Wang, Sucheng Ren, Tiezheng Zhang, Predrag Neskovic, Anand Bhattad, Cihang Xie, Alan Yuille. 2026-02-08. ViT-5: Vision Transformers for The Mid-2020s. https://arxiv.org/abs/2602.08071

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