2 citations · 3 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Towards Few-Shot Learning in the Open World: A Review and Beyond
Hui Xue, Yuexuan An, Yongchun Qin +5
Human intelligence is characterized by our ability to absorb and apply knowledge from the world around us, especially in rapidly acquiring new concepts from minimal examples, under…
Text Image Inpainting via Global Structure-Guided Diffusion Models
Shipeng Zhu, Pengfei Fang, Chenjie Zhu +3
Real-world text can be damaged by corrosion issues caused by environmental or human factors, which hinder the preservation of the complete styles of texts, e.g., texture and struct…