3 papers
cs.CV2025
Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning
Fan Lyu, Linglan Zhao, Chengyan Liu +5
Few-Shot Class-Incremental Learning (FSCIL) focuses on models learning new concepts from limited data while retaining knowledge of previous classes. Recently, many studies have sta…
cs.CV2025
Generalized Category Discovery via Reciprocal Learning and Class-Wise Distribution Regularization
Duo Liu, Zhiquan Tan, Linglan Zhao +3
Generalized Category Discovery (GCD) aims to identify unlabeled samples by leveraging the base knowledge from labeled ones, where the unlabeled set consists of both base and novel…
cs.CV2024
CALA: A Class-Aware Logit Adapter for Few-Shot Class-Incremental Learning
Chengyan Liu, Linglan Zhao, Fan Lyu +3
Few-Shot Class-Incremental Learning (FSCIL) defines a practical but challenging task where models are required to continuously learn novel concepts with only a few training samples…