2 citations · 3 across the 6 of their papers we have counts for
6 papers
Long-Tailed Learning as Multi-Objective Optimization
Weiqi Li, Fan Lyu, Fanhua Shang +2
Real-world data is extremely imbalanced and presents a long-tailed distribution, resulting in models that are biased towards classes with sufficient samples and perform poorly on r…
Constructing Sample-to-Class Graph for Few-Shot Class-Incremental Learning
Fuyuan Hu, Jian Zhang, Fan Lyu +2
Few-shot class-incremental learning (FSCIL) aims to build machine learning model that can continually learn new concepts from a few data samples, without forgetting knowledge of ol…
Dynamic V2X Autonomous Perception from Road-to-Vehicle Vision
Jiayao Tan, Fan Lyu, Linyan Li +4
Vehicle-to-everything (V2X) perception is an innovative technology that enhances vehicle perception accuracy, thereby elevating the security and reliability of autonomous systems.…
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…
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…