Multi View Multi Label

Multi-view multi-label learning tackles the challenge of classifying data with multiple labels and multiple views (e.g., different modalities like text and images) per data point. Current research heavily focuses on addressing the pervasive issue of incomplete data, employing techniques like contrastive learning, autoencoders, and transformers to handle missing views and labels, often within a deep learning framework. These advancements aim to improve the robustness and accuracy of classification in scenarios with noisy or incomplete information, impacting various applications such as image annotation, object recognition, and scene understanding. The development of more efficient and effective algorithms for handling incomplete multi-view multi-label data is a significant area of ongoing research.