1 code implementation • 31 Oct 2023 • Fuyuan Hu, Jian Zhang, Fan Lyu, Linyan Li, Fenglei Xu
Moreover, we design a multi-stage strategy for training S2C model, which mitigates the training challenges posed by limited data in the incremental process.
no code implementations • 29 Oct 2023 • Jiayao Tan, Fan Lyu, Linyan Li, Fuyuan Hu, Tingliang Feng, Fenglei Xu, Rui Yao
Vehicle-to-everything (V2X) perception is an innovative technology that enhances vehicle perception accuracy, thereby elevating the security and reliability of autonomous systems.
no code implementations • 24 Mar 2023 • Hao Chen, Linyan Li, Fan Lyu, Fuyuan Hu, Zhenping Xia, Fenglei Xu
Class-level graph network aims to mitigate the semantic conflict between prototype features of new classes and old classes.
1 code implementation • 6 Mar 2023 • Daofeng Liu, Fan Lyu, Linyan Li, Zhenping Xia, Fuyuan Hu
Rehearsal, retraining on a stored small data subset of old tasks, has been proven effective in solving catastrophic forgetting in continual learning.
no code implementations • 27 Nov 2022 • Kaile Du, Fan Lyu, Linyan Li, Fuyuan Hu, Wei Feng, Fenglei Xu, Xuefeng Xi, Hanjing Cheng
In contrast, the inter-task relationships leverage hard and soft labels from data and a constructed expert network.
1 code implementation • 16 Jul 2022 • Kaile Du, Linyan Li, Fan Lyu, Fuyuan Hu, Zhenping Xia, Fenglei Xu
This paper studies Lifelong Multi-Label (LML) classification, which builds an online class-incremental classifier in a sequential multi-label classification data stream.
1 code implementation • 10 Mar 2022 • Kaile Du, Fan Lyu, Fuyuan Hu, Linyan Li, Wei Feng, Fenglei Xu, Qiming Fu
The key challenges of LML image recognition are the construction of label relationships on Partial Labels of training data and the Catastrophic Forgetting on old classes, resulting in poor generalization.
1 code implementation • 21 Oct 2021 • Liuqing Zhao, Fan Lyu, Fuyuan Hu, Kaizhu Huang, Fenglei Xu, Linyan Li
Sentence-based Image Editing (SIE) aims to deploy natural language to edit an image.
1 code implementation • 16 Jun 2021 • Zihan Ye, Fuyuan Hu, Fan Lyu, Linyan Li, Kaizhu Huang
However, the traditional TL cannot search reliable unseen disentangled representations due to the unavailability of unseen classes in ZSL.
no code implementations • 15 Apr 2019 • Zihan Ye, Fan Lyu, Linyan Li, Qiming Fu, Jinchang Ren, Fuyuan Hu
First, we pre-train a Semantic Rectifying Network (SRN) to rectify semantic space with a semantic loss and a rectifying loss.