While the sandglass attention (SGA) module significantly enhances computational efficiency and captures higher-order spatial-temporal correlations, it may still face limitations in fully modeling intricate relationships within the data. One potential limitation is its reliance on a fixed number of region-level spatial tokens, which may not adequately represent the diversity of spatial interactions in highly dynamic environments. This could lead to oversimplification of the underlying spatial-temporal correlations, particularly in scenarios with complex interdependencies.
To improve the SGA module, one approach could involve implementing a dynamic token selection mechanism that adjusts the number of region-level tokens based on the data's complexity and variability. Additionally, incorporating multi-scale attention mechanisms could allow the model to capture correlations at different spatial and temporal resolutions, providing a more nuanced understanding of the data. Furthermore, integrating graph neural networks (GNNs) with the SGA could enhance its ability to leverage the topological structure of the data, allowing for more sophisticated modeling of spatial relationships.