UniViTAR: Unified Vision Transformer with Native Resolution

• URL: http://arxiv.org/abs/2504.01792v1
• Date: Wed, 02 Apr 2025 14:59:39 GMT
• Title: UniViTAR: Unified Vision Transformer with Native Resolution
• Authors: Limeng Qiao, Yiyang Gan, Bairui Wang, Jie Qin, Shuang Xu, Siqi Yang, Lin Ma,
• Abstract summary: We introduce UniViTAR, a family of homogeneous vision foundation models tailored for unified visual modality and native resolution scenario.<n>A progressive training paradigm is introduced, which strategically combines two core mechanisms.<n>In parallel, a hybrid training framework further synergizes sigmoid-based contrastive loss with feature distillation from a frozen teacher model.
• Score: 37.63387029787732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional Vision Transformer simplifies visual modeling by standardizing input resolutions, often disregarding the variability of natural visual data and compromising spatial-contextual fidelity. While preliminary explorations have superficially investigated native resolution modeling, existing approaches still lack systematic analysis from a visual representation perspective. To bridge this gap, we introduce UniViTAR, a family of homogeneous vision foundation models tailored for unified visual modality and native resolution scenario in the era of multimodal. Our framework first conducts architectural upgrades to the vanilla paradigm by integrating multiple advanced components. Building upon these improvements, a progressive training paradigm is introduced, which strategically combines two core mechanisms: (1) resolution curriculum learning, transitioning from fixed-resolution pretraining to native resolution tuning, thereby leveraging ViT's inherent adaptability to variable-length sequences, and (2) visual modality adaptation via inter-batch image-video switching, which balances computational efficiency with enhanced temporal reasoning. In parallel, a hybrid training framework further synergizes sigmoid-based contrastive loss with feature distillation from a frozen teacher model, thereby accelerating early-stage convergence. Finally, trained exclusively on public datasets, externsive experiments across multiple model scales from 0.3B to 1B demonstrate its effectiveness.

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