Embedding models act as bridges between different data modalities by encoding diverse multimodal information into a shared dense representation space. There have been advancements in embedding models in recent years, driven by progress in large foundation models. However, existing multimodal embedding models are trained on datasets such as MMEB and M-BEIR, with most focus only…

Contrastive Language-Image Pre-training (CLIP) has become important for modern vision and multimodal models, enabling applications such as zero-shot image classification and serving as vision encoders in MLLMs. However, most CLIP variants, including Meta CLIP, are limited to English-only data curation, ignoring a significant amount of non-English content from the worldwide web. Scaling CLIP to include…