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 training-time regularizer, encouraging the encoders to produce features that are compatible under fus...
| Authors | Hanpeng Liu, Yaqian Li, Zidan Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 March 2026 |
| Last updated at source | 9 October 2026 |
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