Document Flattening

Document flattening encompasses techniques that transform multi-dimensional data, such as graphs, videos, or 3D surfaces, into lower-dimensional representations, often 2D or 1D, to improve compatibility with existing machine learning models or enhance computational efficiency. Current research focuses on developing novel flattening methods tailored to specific data types, including graph-based prompting for LLMs, optical flow-guided attention for video editing, and Hilbert curve flattening for image processing, often aiming to optimize for factors like locality preservation, robustness to noise, and generalization performance. These advancements are significant because they enable the application of powerful image-based or sequential models to diverse data modalities, improving performance in tasks ranging from video action recognition to continual learning and enhancing the robustness of deep learning models.