VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization

A novel data augmentation method that generates diverse on-manifold samples through multi-source vicinal transfer to improve model robustness against image corruptions.

Benchmarks for Corruption Invariant Person Re-Identification

The first comprehensive study establishing five ReID benchmarks for learning corruption invariant representations, providing insights on robustness of transformer vs CNN models and cross-dataset generalization.