MM-Prompt: Cross-Modal Prompt Tuning for Continual Visual Question Answering
This addresses modality imbalance in continual learning for visual question answering, offering an incremental improvement over existing prompt-tuning approaches.
The paper tackled the problem of modality imbalance in continual visual question answering by proposing MM-Prompt, a framework with cross-modal prompt query and recovery, which improved accuracy and knowledge retention over prior methods.
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 incorporating 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.