Understanding the Gain from Data Filtering in Multimodal Contrastive Learning

This article likely explores the impact of data filtering techniques on the performance of multimodal contrastive learning models. It probably investigates how removing or modifying certain data points affects the model's ability to learn meaningful representations from different modalities (e.g., images and text). The 'ArXiv' source suggests a research paper, indicating a focus on technical details and experimental results.