30 citations · 51 across the 11 of their papers we have counts for
11 papers
Two-level Graph Network for Few-Shot Class-Incremental Learning
Hao Chen, Linyan Li, Fan Lyu +3
Few-shot class-incremental learning (FSCIL) aims to design machine learning algorithms that can continually learn new concepts from a few data points, without forgetting knowledge…
Centroid Distance Distillation for Effective Rehearsal in Continual Learning
Daofeng Liu, Fan Lyu, Linyan Li +2
Rehearsal, retraining on a stored small data subset of old tasks, has been proven effective in solving catastrophic forgetting in continual learning. However, due to the sampled da…
Multi-Label Continual Learning using Augmented Graph Convolutional Network
Kaile Du, Fan Lyu, Linyan Li +5
Multi-Label Continual Learning (MLCL) builds a class-incremental framework in a sequential multi-label image recognition data stream. The critical challenges of MLCL are the constr…
Class-Incremental Lifelong Learning in Multi-Label Classification
Kaile Du, Linyan Li, Fan Lyu +3
Existing class-incremental lifelong learning studies only the data is with single-label, which limits its adaptation to multi-label data. This paper studies Lifelong Multi-Label (L…
AGCN: Augmented Graph Convolutional Network for Lifelong Multi-label Image Recognition
Kaile Du, Fan Lyu, Fuyuan Hu +4
The Lifelong Multi-Label (LML) image recognition builds an online class-incremental classifier in a sequential multi-label image recognition data stream. The key challenges of LML…
Each Attribute Matters: Contrastive Attention for Sentence-based Image Editing
Liuqing Zhao, Fan Lyu, Fuyuan Hu +3
Sentence-based Image Editing (SIE) aims to deploy natural language to edit an image. Offering potentials to reduce expensive manual editing, SIE has attracted much interest recentl…