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ViCLIP-OT: The First Foundation Vision-Language Model for Vietnamese Image-Text Retrieval with Optimal Transport
🤖AI Summary
Researchers introduced ViCLIP-OT, the first foundation vision-language model specifically designed for Vietnamese image-text retrieval. The model integrates CLIP-style contrastive learning with Similarity-Graph Regularized Optimal Transport (SIGROT) loss, achieving significant improvements over existing baselines with 67.34% average Recall@K on UIT-OpenViIC benchmark.
Key Takeaways
- →ViCLIP-OT is the first foundation vision-language model specifically optimized for Vietnamese image-text retrieval tasks.
- →The model improves upon CLIP by 5.75 percentage points on UIT-OpenViIC and 11.72 percentage points in zero-shot evaluation on Crossmodal-3600.
- →SIGROT loss integration enhances global cross-modal consistency and reduces modality gap issues in low-resource language settings.
- →Extensive testing on three Vietnamese benchmarks demonstrates consistent outperformance in both in-domain and zero-shot scenarios.
- →The approach provides a scalable strategy for cross-modal retrieval systems in underrepresented linguistic contexts beyond Vietnamese.
#vision-language-models#vietnamese-ai#image-text-retrieval#clip#optimal-transport#low-resource-languages#cross-modal#foundation-models#multimedia-ai
Read Original →via arXiv – CS AI
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