MM-Prompt: Cross-Modal Prompt Tuning for Continual Visual Question Answering

Xu Li, Fan Lyu

Keywords: Continual Learning, Visual Question Answering, Prompt Tuning

Abstract: Continual Visual Question Answering (CVQA) based on pre-trained models (PTMs) has achieved promising progress by leveraging prompt tuning to enable continual multi-modal learning. However, most existing methods adopt cross-modal prompt isolation, constructing visual and textual prompts separately, which exacerbates modality imbalance and leads to degraded performance over time. To tackle this issue, we propose MM-Prompt, a novel framework incorporating cross-modal prompt query and cross-modal prompt recovery. The former enables balanced prompt selection by injecting cross-modal signals during query formation, while the latter promotes joint prompt reconstruction through iterative cross-modal interactions, guided by an alignment loss to prevent representational drift. Extensive experiments show that MM-Prompt surpasses prior approaches in accuracy and knowledge retention, while maintaining balanced modality engagement throughout continual learning. Our code will be available.

Primary Area: transfer learning, meta learning, and lifelong learning

Submission Number: 1727