Yangyang Shu

Source-Free Unsupervised Domain Adaptation with Hypothesis Consolidation of Prediction Rationale

2024 · arXiv (Cornell University)

Source-Free Unsupervised Domain Adaptation (SFUDA) is a challenging task where a model needs to be adapted to a new domain without access to target domain labels or source domain data. The primary difficulty in this …

CIT: Rethinking Class-incremental Semantic Segmentation with a Class Independent Transformation

2024 · arXiv (Cornell University)

Class-incremental semantic segmentation (CSS) requires that a model learn to segment new classes without forgetting how to segment previous ones: this is typically achieved by distilling the current knowledge and incorporating the latest data. However, …