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

arXiv – CS AI|HanZpeng Liu, Yaqian Li, Zidan Wang, Shuoxi Zhang, Zonglin Zhao, Zihao Bo, Rinyoichi Takezoe, Kaiwen Long, Kun He|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers propose ITO, a new framework for image-text representation learning that addresses modality gaps through multimodal alignment and training-time fusion. The method outperforms existing baselines across classification, retrieval, and multimodal benchmarks while maintaining efficiency by discarding the fusion module during inference.

Key Takeaways

→ITO framework introduces multimodal multiple alignment to mine diverse image-text correspondences for better supervision.
→Training-time fusion module enforces cross-modal interaction but is discarded at inference to preserve efficiency.
→Method consistently outperforms strong baselines across classification, retrieval, and multimodal benchmarks.
→Training-time fusion acts as structural regularizer, eliminating modality gaps and stabilizing training dynamics.
→Framework prevents early saturation commonly observed in aggressive contrastive learning approaches.

#multimodal-ai #computer-vision #representation-learning #contrastive-learning #image-text #machine-learning #research

Read Original →via arXiv – CS AI

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