arXiv:2605.20674v1 Announce Type: new Abstract: We introduce CoMET, \textit{\textbf{C}omposing \textbf{M}odality \textbf{E}ncoders with \textbf{T}abular foundation models}, a simple yet highly competitive method for multimodal classification: pass each modality through a frozen pre-trained backbone, compress the resulting embeddings with PCA, and concatenate as input into a Tabular Foundation Model (TFM) for prediction. We show that PCA alone suffices to act as an adaptor yielding strong, robust performance across modalities. When the \texttt{CLS} tokens of the foundation model align poorly wi
The paper leverages recent advancements in multimodal AI and foundation models, addressing the ongoing challenge of efficient and robust multimodal classification without requiring extensive fine-tuning.
This development allows for more rapid and less resource-intensive deployment of general-purpose multimodal AI systems, accelerating development cycles for various applications.
A simpler, more efficient compositional approach for multimodal classification is now demonstrated as highly competitive, potentially reducing the computational and data requirements for integrating diverse data types.
- · AI researchers
- · Companies with diverse data modalities
- · Developers of multimodal AI applications
- · Methods requiring extensive fine-tuning
- · Specialized, highly complex multimodal architectures
Easier and faster integration of various data types (text, image, tabular) into AI models.
Accelerated development and deployment of agentic systems capable of processing and reasoning over diverse information sources.
Enhanced capabilities for AI agents to understand and interact with the world through multiple senses, leading to more robust autonomous systems.
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