Cross-modal Learning for Image Understanding: Investigating cross-modal learning techniques for understanding images through multiple modalities such as text or audio descriptions

Abstract

Cross-modal learning, which aims to leverage information from multiple modalities, has emerged as a promising approach for enhancing image understanding. By integrating textual or auditory information with visual data, cross-modal learning enables machines to better comprehend the content and context of images. This paper provides a comprehensive review of cross-modal learning techniques for image understanding, focusing on the fusion of textual and visual information. We discuss the challenges and opportunities in cross-modal learning, explore various methodologies, and highlight their applications in real-world scenarios. Additionally, we present a critical analysis of existing evaluation metrics and datasets, emphasizing the need for standardized benchmarks to facilitate comparative studies. Our findings suggest that cross-modal learning holds great potential for advancing image understanding, with implications for diverse fields such as multimedia retrieval, image captioning, and assistive technologies.