Pseudo-set Frequency Refinement architecture for fine-grained few-shot class-incremental learning

Zicheng Pan, Weichuan Zhang, Xiaohan Yu, Miaohua Zhang, Yongsheng Gao

Abstract: Highlights•Developed a pseudo-set frequency refinement method for fine-grained FSCIL tasks.•Separated input image into high- and low-frequency components for better learning.•Enhanced the model’s discrimination and generalization abilities with the new method.•Introduced a pseudo-set training strategy to mimic incremental learning scenarios.•Created FSCIL benchmarks using four fine-grained datasets for the first time.