PercepT discovers perceptual topic clusters from vision-language data via unsupervised training and maps images to them with attention pooling, reporting silhouette 0.97 and AUC 0.94 on ArtELingo.
No Culture Left Behind:
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The survey identifies a key tension in multilingual vision-language models between language neutrality via contrastive learning and cultural awareness via diverse data, with most benchmarks relying on translation-based evaluation.
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Beyond Semantics: Modeling Factual and Affective Perceptual Experiences from Vision-Language Data
PercepT discovers perceptual topic clusters from vision-language data via unsupervised training and maps images to them with attention pooling, reporting silhouette 0.97 and AUC 0.94 on ArtELingo.
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Multilingual Vision-Language Models, A Survey
The survey identifies a key tension in multilingual vision-language models between language neutrality via contrastive learning and cultural awareness via diverse data, with most benchmarks relying on translation-based evaluation.