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.
Domain-adversarial training of neural networks.Journal of machine learning research, 17(59):1–35, 2016
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PEPR reframes learning with privileged event data as predicting latent event features from RGB to improve domain generalization in object detection and segmentation without direct cross-modal alignment.
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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.
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PEPR: Privileged Event-based Predictive Regularization for Domain Generalization
PEPR reframes learning with privileged event data as predicting latent event features from RGB to improve domain generalization in object detection and segmentation without direct cross-modal alignment.