PL-FSCIL: Harnessing the Power of Prompts for Few-Shot Class-Incremental Learning

no code implementations • 26 Jan 2024 • Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Li Li, Xin Ning

In this paper, we propose a novel approach called Prompt Learning for FSCIL (PL-FSCIL), which harnesses the power of prompts in conjunction with a pre-trained Vision Transformer (ViT) model to address the challenges of FSCIL effectively.

Few-Shot Class-Incremental Learning Incremental Learning

A Survey on Few-Shot Class-Incremental Learning

no code implementations • 17 Apr 2023 • Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari

Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective.

Few-Shot Class-Incremental Learning Few-Shot Learning +6

NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks

no code implementations • 9 Mar 2023 • Wenkai Tan, Justus Renkhoff, Alvaro Velasquez, Ziyu Wang, Lusi Li, Jian Wang, Shuteng Niu, Fan Yang, Yongxin Liu, Houbing Song

Our work could provide a useful tool to defend against certain adversarial attacks on deep neural networks.