QMPN: A Quality-Aware Memory Prompting Network for Few-Shot Multimodal Aspect-Based Sentiment Analysis

Multimodal aspect-based sentiment analysis (MABSA) predicts the sentiment polarity associated with a specified aspect by jointly exploiting textual and visual information. Existing models may be sensitive to limited prompting examples, cross-modal noise, and unreliable generated context. This paper proposes QMPN, Quality-Aware Memory Prompting Network, that stores sample-specific prompts derived from a small support set, retrieves relevant prompting evidence for each query, and uses the retrieved prompts to guide aspect-aware context generation. A task-oriented quality gate, learned indirectly through the sentiment classification objective, controls the contribution of the generated context to the final prediction. Under the fixed protocol used in this study, QMPN employs 50 labeled support instances for prompt-memory construction and achieves 78.6% accuracy and 74.8% macro-F1 on Twitter-2015, and 72.0% accuracy and 70.5% macro-F1 on Twitter-2017. Relative to the variant without context generation, the complete model improves accuracy/macro-F1 by 3.31/2.83 percentage points on Twitter-2015 and 3.84/3.22 percentage points on Twitter-2017. Ablation and parameter analyses further show the contributions of memory retrieval, adaptive prompt selection, context generation, and quality-aware fusion. Because the evaluation is limited to two historical Twitter benchmarks and a single fixed seed, the reported results should be interpreted within this experimental scope rather than as evidence of universal cross-domain generalization.