MissMAC-Bench: Building Solid Benchmark for Missing Modality Issue in Robust Multimodal Affective Computing

MissMAC-Bench is a new benchmark for evaluating how multimodal affective computing systems handle missing modality data. It establishes fair, unified evaluation standards based on cross‑modal synergy, requiring models to perform without prior missing data during training and to handle both complete and incomplete inputs. The benchmark includes protocols for fixed and random missing patterns at dataset and instance levels, and experiments on three language models across four datasets demonstrate its effectiveness.

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• llms
• multimodal
• benchmarks