Multimodal Data Augmentation

Multimodal data augmentation aims to improve the performance of machine learning models that process multiple data types (e.g., images and text) by artificially expanding training datasets. Current research focuses on developing methods that generate realistic and semantically consistent augmented data, often leveraging techniques like mixing, attribute manipulation guided by knowledge bases, and feature-space transformations. These advancements are significant because they address the limitations of existing multimodal datasets, leading to improved model accuracy and generalization across various applications, including 3D object recognition, image captioning, and visual question answering.