To this end, we propose SpatialPIN, a framework that utilizes progressive prompting and interactions between VLMs and 2D/3D foundation models as "free lunch" to enhance spatial reasoning capabilities

One may naturally ask, "When would these explanations be helpful?"

Recently, data-driven approaches for weather forecasting based on deep learning have shown great promise, achieving accuracies that are competitive with operational systems. However, those methods often employ complex, customized architectures without sufficient ablation analysis, making it difficult to understand what truly contributes to their success.

Code is available at https://github.com/wl-zhao/UniPC . In this paper, we propose a training-free framework for the fast sampling of DPMs called UniPC .

In this paper, we introduce a new class of structured matrices, which unifies and generalizes structured classes from previous works.