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New method enhances Bengali image captioning by aligning visual patches with text

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for Bengali image captioning that improves alignment between visual elements and text, particularly in low-resource scenarios. The approach utilizes a tri-loss objective combining patch-alignment, InfoNCE, and optimal transport to enhance the correspondence between real and synthetic image-text pairs. This technique significantly boosts performance on benchmark datasets like Flickr30k-1k and MSCOCO-1k, outperforming existing contrastive learning baselines. AI

IMPACT Improves low-resource language capabilities in vision-language models, potentially enabling better cross-modal applications in underserved languages.

RANK_REASON The item is a research paper detailing a new method for image captioning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method enhances Bengali image captioning by aligning visual patches with text

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The item is a research paper detailing a new method for image captioning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Riad Ahmed Anonto, Sardar Md. Saffat Zabin, M. Saifur Rahman ·

    Align Where the Words Look: Cross-Attention-Guided Patch Alignment with Contrastive and Transport Regularization for Bengali Captioning

    arXiv:2509.18369v2 Announce Type: replace Abstract: Adapting vision-language models in low-resource languages remains challenging, as they often produce fluent text about the wrong objects. This stems from scarce paired data, translation pivots that break alignment, and English-c…

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