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SFTformer: A Spatial-Frequency-Temporal Correlation-Decoupling Transformer for Radar Echo Extrapolation
Feb 28, 2024
Extrapolating future weather radar echoes from past observations is a complex task vital for precipitation nowcasting. The spatial morphology and temporal evolution of radar echoes exhibit a certain degree of correlation, yet they also possess independent characteristics. {Existing methods learn unified spatial and temporal representations in a highly coupled feature space, emphasizing the correlation between spatial and temporal features but neglecting the explicit modeling of their independent characteristics, which may result in mutual interference between them.} To effectively model the spatiotemporal dynamics of radar echoes, we propose a Spatial-Frequency-Temporal correlation-decoupling Transformer (SFTformer). The model leverages stacked multiple SFT-Blocks to not only mine the correlation of the spatiotemporal dynamics of echo cells but also avoid the mutual interference between the temporal modeling and the spatial morphology refinement by decoupling them. Furthermore, inspired by the practice that weather forecast experts effectively review historical echo evolution to make accurate predictions, SFTfomer incorporates a joint training paradigm for historical echo sequence reconstruction and future echo sequence prediction. Experimental results on the HKO-7 dataset and ChinaNorth-2021 dataset demonstrate the superior performance of SFTfomer in short(1h), mid(2h), and long-term(3h) precipitation nowcasting.
Thinking Like an Expert:Multimodal Hypergraph-of-Thought (HoT) Reasoning to boost Foundation Modals
Aug 11, 2023
Reasoning ability is one of the most crucial capabilities of a foundation model, signifying its capacity to address complex reasoning tasks. Chain-of-Thought (CoT) technique is widely regarded as one of the effective methods for enhancing the reasoning ability of foundation models and has garnered significant attention. However, the reasoning process of CoT is linear, step-by-step, similar to personal logical reasoning, suitable for solving general and slightly complicated problems. On the contrary, the thinking pattern of an expert owns two prominent characteristics that cannot be handled appropriately in CoT, i.e., high-order multi-hop reasoning and multimodal comparative judgement. Therefore, the core motivation of this paper is transcending CoT to construct a reasoning paradigm that can think like an expert. The hyperedge of a hypergraph could connect various vertices, making it naturally suitable for modelling high-order relationships. Inspired by this, this paper innovatively proposes a multimodal Hypergraph-of-Thought (HoT) reasoning paradigm, which enables the foundation models to possess the expert-level ability of high-order multi-hop reasoning and multimodal comparative judgement. Specifically, a textual hypergraph-of-thought is constructed utilizing triple as the primary thought to model higher-order relationships, and a hyperedge-of-thought is generated through multi-hop walking paths to achieve multi-hop inference. Furthermore, we devise a visual hypergraph-of-thought to interact with the textual hypergraph-of-thought via Cross-modal Co-Attention Graph Learning for multimodal comparative verification. Experimentations on the ScienceQA benchmark demonstrate the proposed HoT-based T5 outperforms CoT-based GPT3.5 and chatGPT, which is on par with CoT-based GPT4 with a lower model size.