For a long time, due to the high heterogeneity in structure and semantics
among various spatiotemporal modal data, the joint interpretation of multimodal
spatiotemporal data has been an extremely challenging problem. The primary
challenge resides in striking a trade-off between the cohesion and autonomy of
diverse modalities, and this trade-off exhibits a progressively nonlinear
nature as the number of modalities expands. We introduce the Language as
Reference Framework (LaRF), a fundamental principle for constructing a
multimodal unified model, aiming to strike a trade-off between the cohesion and
autonomy among different modalities. We propose a multimodal spatiotemporal
general artificial intelligence model, called AllSpark. Our model integrates
thirteen different modalities into a unified framework, including 1D (text,
code), 2D (RGB, infrared, SAR, multispectral, hyperspectral, tables, graphs,
trajectory, oblique photography), and 3D (point clouds, videos) modalities. To
achieve modal cohesion, AllSpark uniformly maps diverse modal features to the
language modality. In addition, we design modality-specific prompts to guide
multi-modal large language models in accurately perceiving multimodal data. To
maintain modality autonomy, AllSpark introduces modality-specific encoders to
extract the tokens of various spatiotemporal modalities. And modal bridge is
employed to achieve dimensional projection from each modality to the language
modality. Finally, observing a gap between the model's interpretation and
downstream tasks, we designed task heads to enhance the model's generalization
capability on specific downstream tasks. Experiments indicate that AllSpark
achieves competitive accuracy in modalities such as RGB and trajectory compared
to state-of-the-art models.

通过引入语言参考框架 (LaRF) 和 AllSpark 模型，将多模态时空数据的联合解释问题化为在各模态之间达成连贯性和自主性之间的权衡，并且实验结果表明 AllSpark 在 RGB 和轨迹等模态上相比最先进模型具有竞争力的准确度。