AllSpark: A Multimodal Spatio-Temporal General Intelligence Model with Thirteen Modalities

• URL: http://arxiv.org/abs/2401.00546v2
• Date: Sun, 25 Feb 2024 09:18:20 GMT
• Title: AllSpark: A Multimodal Spatio-Temporal General Intelligence Model with Thirteen Modalities
• Authors: Run Shao, Cheng Yang, Qiujun Li, Qing Zhu, Yongjun Zhang, YanSheng Li, Yu Liu, Yong Tang, Dapeng Liu, Shizhong Yang, Haifeng Li
• Abstract summary: We introduce the Language as Reference Framework (LaRF), a fundamental principle constructing a multimodal unified model. We propose a multimodaltemporal general artificial intelligence model, called AllSpark. Our model integrates thirteen different modalities into a unified framework, including 1D (text, code), 2D (RGB, oblique, infrared, multispectral, hyperspectral), tables, graphs, clouds, videos.
• Score: 21.735142277761383
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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