The ProMMA framework evaluates missing modalities at input using a dedicated evaluator, then applies modality-invariant prompt disentanglement, mutual-information dynamic weighting, and multi-level residual prompt connections to deliver state-of-the-art robust performance on sentiment analysis under
Ch sims: A chinese multimodal sentiment analysis dataset with fine-grained annotation of modality,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Evaluation Before Generation: A Paradigm for Robust Multimodal Sentiment Analysis with Missing Modalities
The ProMMA framework evaluates missing modalities at input using a dedicated evaluator, then applies modality-invariant prompt disentanglement, mutual-information dynamic weighting, and multi-level residual prompt connections to deliver state-of-the-art robust performance on sentiment analysis under