ITO: Multi-View Alignment and Training-Time Fusion for Image-Text Pretraining

Image–text contrastive pretraining has become a dominant paradigm for visual representation learning, yet existing methods often yield representations that remain partially organized by modality rather than by semantics. We propose ITO, a framework addressing this limitation through two complementary mechanisms with distinct roles. Multimodal multiple alignment enriches supervision by constructing diverse cross-modal correspondences from multi-view image augmentations, providing the primary source of discriminative gain. A lightweight training-time multimodal fusion module then acts as a geometric regularizer, encouraging the encoders to produce features that are compatible under fusion and thereby reducing modality-induced separation. Crucially, the fusion module is discarded at inference, preserving the efficiency of standard dual-encoder architectures while incurring training-time overhead only. Extensive experiments across pretraining scales from millions to billions of image–text pairs show that ITO consistently outperforms strong baselines on classification, retrieval, and multimodal benchmarks. Our analysis further reveals that beyond accuracy gains, training-time fusion plays a stabilizing role in optimization, mitigating the late-stage overfitting commonly observed in aggressive contrastive learning.