A large-scale benchmark finds that recent multimodal domain generalization methods give only marginal gains over a plain ERM baseline, with no method winning consistently and all degrading sharply under corruption or missing modalities.
Generalizing from several related classification tasks to a new unlabeled sample
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Are We Making Progress in Multimodal Domain Generalization? A Comprehensive Benchmark Study
A large-scale benchmark finds that recent multimodal domain generalization methods give only marginal gains over a plain ERM baseline, with no method winning consistently and all degrading sharply under corruption or missing modalities.