Scaling Hybrid Batch Training for Multimodal Language Models in Zero-Shot Cross-Lingual Image-Text Retrieval on XQuAD

There has been a recent spike in interest in multi-modal Language and Vision problems. On the language side, most of these models primarily focus on English since most multi-modal datasets are monolingual. We try to bridge this gap with a zero-shot approach for learning multi-modal representations using cross-lingual pre-training on the text side. We present a simple yet practical approach for building a cross-lingual image retrieval model which trains on a monolingual training dataset but can be used in a zero-shot cross-lingual fashion during inference. We also introduce a new objective func

Research goal: How does the hybrid batch training strategy scale to multimodal language models like CLIP or BLIP when evaluated on zero-shot cross-lingual image-text retrieval tasks using the XQuAD benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.1/10.