Parameter-Efficient Adapter Tuning for Tabular-Image Multimodal Learning

Abstract:Tabular-image multimodal learning aims to improve predictive modeling by jointly using structured tabular attributes and visual data. Although pretrained encoders provide strong modality-specific representations, full fine-tuning can be computationally expensive, while keeping encoders frozen may limit task-specific adaptation. We propose the Tabular-Image Adapter (TI-Adapter), a modality-specific adapter-based fine-tuning framework for efficient multimodal adaptation. TI-Adapter freezes the pretrained tabular encoder and learns an adapter after the extracted tabular embedding, while adapting the image branch with embedding-level and bottleneck-level adapters instead of full fine-tuning. Experiments on 20 tabular-image datasets show that TI-Adapter achieves competitive or better predictive performance than full fine-tuning while using substantially fewer trainable parameters. Ablation studies further demonstrate the importance of adapter placement for balancing performance and practical efficiency.

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2606.11682 [cs.CV]

(or arXiv:2606.11682v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.11682 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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