ITO: Images and Texts as One via Synergizing Multiple Alignment and Training-Time Fusion

• URL: http://arxiv.org/abs/2603.02767v2
• Date: Wed, 04 Mar 2026 01:56:54 GMT
• Title: ITO: Images and Texts as One via Synergizing Multiple Alignment and Training-Time Fusion
• Authors: HanZpeng Liu, Yaqian Li, Zidan Wang, Shuoxi Zhang, Zonglin Zhao, Zihao Bo, Rinyoichi Takezoe, Kaiwen Long, Kun He,
• Abstract summary: ITO is a framework addressing the limitation through two synergistic mechanisms.<n>We show that ITO consistently outperforms strong baselines across classification, retrieval, and multimodal benchmarks.<n>Our analysis reveals that while multiple alignment drives discriminative power, training-time fusion acts as a critical structural regularizer.
• Score: 14.791563751107502
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. We propose ITO, a framework addressing this limitation through two synergistic mechanisms. Multimodal multiple alignment enriches supervision by mining diverse image-text correspondences, while a lightweight training-time multimodal fusion module enforces structured cross-modal interaction. Crucially, the fusion module is discarded at inference, preserving the efficiency of standard dual-encoder architectures. Extensive experiments show that ITO consistently outperforms strong baselines across classification, retrieval, and multimodal benchmarks. Our analysis reveals that while multiple alignment drives discriminative power, training-time fusion acts as a critical structural regularizer -- eliminating the modality gap and stabilizing training dynamics to prevent the early saturation often observed in aggressive contrastive learning.

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