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Unified Vision-Language Modeling via Concept Space Alignment

arXiv – CS AI|Yifu Qiu, Paul-Ambroise Duquenne, Holger Schwenk||2 views
🤖AI Summary

Researchers introduce V-SONAR, a vision-language embedding system that extends text-only SONAR to support 1500+ languages with vision capabilities. The system demonstrates state-of-the-art performance on video captioning and multilingual vision tasks through V-LCM, which combines vision and language processing in a unified framework.

Key Takeaways
  • V-SONAR extends SONAR's multilingual capabilities to vision tasks, supporting 1500 text and 177 speech languages.
  • The system achieves superior performance on video captioning with BLEU scores of 23.9 vs 19.6 on DREAM-1K and 39.0 vs 30.0 on PE-VIDEO.
  • V-LCM demonstrates zero-shot visual concept understanding capabilities using only English text training data.
  • The model significantly outperforms existing vision-language models across 61 out of 62 tested languages.
  • The approach uses a unified latent embedding sequence for both vision and language inputs with next-embedding prediction training.
Read Original →via arXiv – CS AI
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