Abstract: Few-shot class incremental learning presents a promising approach for the adaptation of detect models to evolving scenarios where the diversity of cigarette defect classes dynamically ...

Abstract: This work focuses on a practical knowledge transfer task defined as Source-Free Unsupervised Domain Adaptation (SFUDA), where only a well-trained source model and unlabeled target data are ...