The paper introduces MMHCL, a setting where modalities train on heterogeneous category sets, and CSCF, a model using semantic alignment, uncertainty-based dominance selection, and class-similarity fusion to recognize the complete class space.
Uncertainty-aware pseudo-labeling and dual graph driven network for incomplete multi-view multi-label classification
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Class Similarity-Based Multimodal Classification under Heterogeneous Category Sets
The paper introduces MMHCL, a setting where modalities train on heterogeneous category sets, and CSCF, a model using semantic alignment, uncertainty-based dominance selection, and class-similarity fusion to recognize the complete class space.