3 papers
cs.LG2024
PEARL: Input-Agnostic Prompt Enhancement with Negative Feedback Regulation for Class-Incremental Learning
Yongchun Qin, Pengfei Fang, Hui Xue
Class-incremental learning (CIL) aims to continuously introduce novel categories into a classification system without forgetting previously learned ones, thus adapting to evolving…
cs.LG2024
On Distilling the Displacement Knowledge for Few-Shot Class-Incremental Learning
Pengfei Fang, Yongchun Qin, Hui Xue
Few-shot Class-Incremental Learning (FSCIL) addresses the challenges of evolving data distributions and the difficulty of data acquisition in real-world scenarios. To counteract th…
cs.CV2024
SVasP: Self-Versatility Adversarial Style Perturbation for Cross-Domain Few-Shot Learning
Wenqian Li, Pengfei Fang, Hui Xue
Cross-Domain Few-Shot Learning (CD-FSL) aims to transfer knowledge from seen source domains to unseen target domains, which is crucial for evaluating the generalization and robustn…