Text as Any-Modality for Zero-Shot Classification by Consistent Prompt Tuning

• URL: http://arxiv.org/abs/2508.06382v1
• Date: Fri, 08 Aug 2025 15:13:05 GMT
• Title: Text as Any-Modality for Zero-Shot Classification by Consistent Prompt Tuning
• Authors: Xiangyu Wu, Feng Yu, Yang Yang, Jianfeng Lu,
• Abstract summary: We present Text as Any-Modality by Consistent Prompt Tuning (TaAM-CPT), a scalable approach for constructing a general representation model.<n>TaAM-CPT comprises modality prompt pools, text construction, and modality-aligned text encoders from pre-trained models.<n>To harmonize the learning across different modalities, TaAM-CPT designs intra- and inter-modal learning objectives.
• Score: 10.744123073654544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The integration of prompt tuning with multimodal learning has shown significant generalization abilities for various downstream tasks. Despite advancements, existing methods heavily depend on massive modality-specific labeled data (e.g., video, audio, and image), or are customized for a single modality. In this study, we present Text as Any-Modality by Consistent Prompt Tuning (TaAM-CPT), a scalable approach for constructing a general representation model toward unlimited modalities using solely text data. TaAM-CPT comprises modality prompt pools, text construction, and modality-aligned text encoders from pre-trained models, which allows for extending new modalities by simply adding prompt pools and modality-aligned text encoders. To harmonize the learning across different modalities, TaAM-CPT designs intra- and inter-modal learning objectives, which can capture category details within modalities while maintaining semantic consistency across different modalities. Benefiting from its scalable architecture and pre-trained models, TaAM-CPT can be seamlessly extended to accommodate unlimited modalities. Remarkably, without any modality-specific labeled data, TaAM-CPT achieves leading results on diverse datasets spanning various modalities, including video classification, image classification, and audio classification. The code is available at https://github.com/Jinx630/TaAM-CPT.

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