These components enhance the model’s ability to distinguish unknown class samples during online adaptation by amplifying the entropy difference between known and unknown samples.

Our analysis shows that the entropy difference between known and unknown samples in the target domain strongly correlates with MM-OSTTA performance. We propose two key components: Unknown-aware Adaptive Entropy Optimization (UAE) and Adaptive Modality Prediction Discrepancy Optimization (AMP).