4 papers
Knowledge Graph Enhanced Generative Multi-modal Models for Class-Incremental Learning
Xusheng Cao, Haori Lu, Linlan Huang +3
Continual learning in computer vision faces the critical challenge of catastrophic forgetting, where models struggle to retain prior knowledge while adapting to new tasks. Although…
Restoring Forgotten Knowledge in Non-Exemplar Class Incremental Learning through Test-Time Semantic Evolution
Haori Lu, Xusheng Cao, Linlan Huang +3
Continual learning aims to accumulate knowledge over a data stream while mitigating catastrophic forgetting. In Non-exemplar Class Incremental Learning (NECIL), forgetting arises d…
Learning Part Knowledge to Facilitate Category Understanding for Fine-Grained Generalized Category Discovery
Enguang Wang, Zhimao Peng, Zhengyuan Xie +3
Generalized Category Discovery (GCD) aims to classify unlabeled data containing both seen and novel categories. Although existing methods perform well on generic datasets, they str…
Class-Incremental Learning with CLIP: Adaptive Representation Adjustment and Parameter Fusion
Linlan Huang, Xusheng Cao, Haori Lu +1
Class-incremental learning is a challenging problem, where the goal is to train a model that can classify data from an increasing number of classes over time. With the advancement…