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arXiv · 2509.17562

Visual Instruction Pretraining for Domain-Specific Foundation Models

Yuxuan Li·Yicheng Zhang·Wenhao Tang·Yimian Dai·Ming-Ming Cheng·Xiang Li·Jian Yang
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Abstract

Modern computer vision is converging on a closed loop in which perception, reasoning and generation mutually reinforce each other. However, this loop remains incomplete: the top-down influence of high-level reasoning on the foundational learning of low-level perceptual features is not yet underexplored. This paper addresses this gap by proposing a new paradigm for pretraining foundation models in downstream domains. We introduce Visual insTruction Pretraining (ViTP), a novel approach that directly leverages reasoning to enhance perception. ViTP embeds a Vision Transformer (ViT) backbone within a Vision-Language Model and pretrains it end-to-end using a rich corpus of visual instruction data curated from target downstream domains. ViTP is powered by our proposed Visual Robustness Learning (VRL), which compels the ViT to learn robust and domain-relevant features from a sparse set of visual tokens. Extensive experiments on 16 challenging remote sensing and medical imaging benchmarks demonstrate that ViTP establishes new state-of-the-art performance across a diverse range of downstream tasks. The code is available at https://github.com/zcablii/ViTP.

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Yuxuan Li, Yicheng Zhang, Wenhao Tang, Yimian Dai, Ming-Ming Cheng, Xiang Li, Jian Yang. 2026-02-26. Visual Instruction Pretraining for Domain-Specific Foundation Models. https://arxiv.org/abs/2509.17562

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