Self-Supervised Learning for Multi-Modal Data

Abstract

Self-supervised learning (SSL) for multi-modal data represents a transformative approach to harnessing the rich, complementary information inherent in diverse data types such as images, text, and audio. By developing methods that learn joint representations, SSL can enable more effective integration and understanding across modalities, enhancing performance in tasks like classification, retrieval, and clustering. This paper delves into novel strategies for multi-modal representation learning, emphasizing the potential of cross-modal retrieval and advanced fusion techniques. These advancements can significantly improve the robustness and generalization of models, paving the way for more sophisticated and versatile multi-modal applications.