CoRe: Coarse-to-Fine Learning for Few-Shot Multimodal Sentiment Regression

Multimodal sentiment regression (MSR) integrates complementary information across modalities to model sentiment polarity and intensity in a continuous sentiment space. However, existing MSR methods typically rely on large-scale annotated datasets, which are costly and difficult to obtain. While few-shot MSR offers a promising solution, it still faces three key challenges: (i) limited training sets may fail to preserve the underlying continuous sentiment distribution; (ii) pretrained models are typically better at semantic understanding than at numerical regression; and (iii) conventional regression losses focus on point-wise errors, overlooking distributional discrepancies. To address these challenges, we make the first attempt to investigate few-shot MSR and propose CoRe, a coarse-to-fine learning framework. Specifically, CoRe introduces a continuous distribution sampling strategy to construct a representative few-shot training set that closely approximates the global sentiment distribution. It further reformulates sentiment regression as a coarse-to-fine prediction process, progressively refining pretrained affective semantics into continuous sentiment predictions, with a heterogeneous multimodal graph capturing cross-modal and contextual dependencies. Moreover, CoRe employs a distance-aware objective to align predicted sentiment distributions with soft targets, thereby reducing distributional discrepancies. Extensive experiments on four datasets demonstrate that CoRe outperforms state-of-the-art baselines, reducing MAE by 6.13% and improving Corr by 5.03% on average.