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

Xiangyu Wu, Feng Yu, Yang Yang, Jianfeng Lu

Keywords: Multimodal Learning ; Prompt Learning; Zero-shot Classification;

TL;DR: A general representation model toward unlimited modalities without modality-specific labeled data.

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 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 (Kinetic-400/600/700), image classification (MSCOCO, VOC2007, NUSWIDE, VOC2012, Objects365), and audio classification (ESC50, US8K). The code is available at https://anonymous.4open.science/r/TaAM-CPT-0EA6.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 11035