Non Textual

Research on non-textual data focuses on effectively integrating and leveraging information beyond textual content to improve various machine learning tasks. Current efforts concentrate on incorporating visual features (e.g., layout, color, images) and metadata (e.g., user interactions, social media annotations) into models, often adapting existing architectures like transformers or developing novel approaches like asymmetric consistency learning and uni-attention mechanisms for multimodal data fusion. This work is significant because it enhances the accuracy and robustness of systems in diverse applications, including recommendation systems, entity recognition from document images, and click-through rate prediction, ultimately leading to more comprehensive and insightful analyses.