Performance of Multimodal Models in Zero-Shot Cross-Lingual Text-Image Retrieval on MMMU Benchmark

Abstract

Abstract: Contrastive Language-Image Pretraining (CLIP) is widely used to train models to align images and texts in a common embedding space by mapping them to fixed-sized vectors. These models are key to multimodal information retrieval and related tasks. However, CLIP models generally underperform in text-only tasks compared to specialized text models. This creates inefficiencies for information retrieval systems that keep separate embeddings and models for text-only and multimodal tasks. We propose a novel, multi-task contrastive training method to address this issue, which we use to train the jina-c

Research Question

How do multimodal models like CLIP perform on zero-shot cross-lingual text-image retrieval tasks compared to text-only models like mBERT when evaluated on the MMMU benchmark?

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