Instance-wise Adversarial Pretraining for Vision-Language Transformer Generalization on COCO Captions

Abstract

Abstract: Urdu, spoken by over 250 million people, remains critically under-served in multimodal and vision-language research. The absence of large-scale, high-quality datasets has limited the development of Urdu-capable systems and reinforced biases in multilingual vision-language models trained primarily on high-resource languages. To address this gap, we present COCO-Urdu, a large-scale image-caption dataset derived from MS COCO, containing 59,000 images and 319,000 Urdu captions selected through stratified sampling to preserve the original distribution. Captions were translated using SeamlessM4T v2

Research Question

Does instance-wise adversarial pretraining improve the out-of-distribution generalization of vision-language transformers on the COCO Captions benchmark relative to mixup-based data augmentation?

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