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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||3 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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