CrossFlowDG bridges the modality gap in domain generalization by learning a continuous transformation that moves image embeddings to matching text embeddings using noise-free cross-modal flow matching.
Unbiased met- ric learning: On the utilization of multiple datasets and web images for softening bias
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
CrossFlowDG: Bridging the Modality Gap with Cross-modal Flow Matching for Domain Generalization
CrossFlowDG bridges the modality gap in domain generalization by learning a continuous transformation that moves image embeddings to matching text embeddings using noise-free cross-modal flow matching.