6 papers
Sharpness-aware Dynamic Anchor Selection for Generalized Category Discovery
Zhimao Peng, Enguang Wang, Fei Yang +2
Generalized category discovery (GCD) is an important and challenging task in open-world learning. Specifically, given some labeled data of known classes, GCD aims to cluster unlabe…
Logo-VGR: Visual Grounded Reasoning for Open-world Logo Recognition
Zichen Liang, Jingjing Fei, Jie Wang +6
Recent advances in multimodal large language models (MLLMs) have been primarily evaluated on general-purpose benchmarks, while their applications in domain-specific scenarios, such…
KAC: Kolmogorov-Arnold Classifier for Continual Learning
Yusong Hu, Zichen Liang, Fei Yang +3
Continual learning requires models to train continuously across consecutive tasks without forgetting. Most existing methods utilize linear classifiers, which struggle to maintain a…
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…