SOTAlign: Semi-Supervised Alignment of Unimodal Vision and Language Models via Optimal Transport
arXiv:2602.23353v2 Announce Type: replace Abstract: The Platonic Representation Hypothesis posits that neural networks trained on different modalities converge toward a shared statistical model of the world. Recent work exploits this convergence by aligning frozen pretrained vision and language models with lightweight alignment layers, but typically relies on contrastive losses and millions of paired samples. In this work, we ask whether meaningful alignment can be achieved with substantially less supervision. We introduce a semi-supervised setting in which pretrained unimodal encoders are ali
The proliferation of unimodal AI models and the increasing computational cost of fully supervised alignment necessitate more efficient, resource-lean methods of integration.
This research suggests a pathway to significantly reduce the data and computational resources required to integrate disparate AI models, enabling broader and more cost-effective AI development.
Successful semi-supervised alignment could lower the bar for developing multimodal AI systems, potentially democratizing access and accelerating innovation beyond large, resource-rich organizations.
- · AI researchers
- · Smaller AI development firms
- · SaaS providers leveraging multimodal AI
- · Edge AI computing
- · Companies reliant solely on massive, proprietary paired datasets for multimodal
Reduced data and computational requirements for training strong multimodal AI models.
Faster development and deployment of new AI applications, especially those requiring fusion of different data types.
Increased competition and diversification in the AI industry as barriers to entry for advanced model development are lowered.
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Read at arXiv cs.LG