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.
Improving generalized zero-shot learning by exploring the diverse semantics from external class names
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.