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Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator

Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator

IEEE International Conference on Robotics and Automation (ICRA), 2025
5 February 2025
Wei-Bin Kou
Guangxu Zhu
Rongguang Ye
Shuai Wang
Ming Tang
Yik-Chung Wu
ArXiv (abs)PDFHTML

Papers citing "Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator"

3 / 3 papers shown
Title
iMacHSR: Intermediate Multi-Access Heterogeneous Supervision and Regularization Scheme Toward Architecture-Agnostic Training
iMacHSR: Intermediate Multi-Access Heterogeneous Supervision and Regularization Scheme Toward Architecture-Agnostic Training
Wei-Bin Kou
Guangxu Zhu
Yichen Jin
Bingyang Cheng
Shuai Wang
Ming Tang
Yik-Chung Wu
197
0
0
01 May 2025
FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving
FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving
Wei-Bin Kou
Guangxu Zhu
Bingyang Cheng
Shuai Wang
Ming Tang
Yik-Chung Wu
201
0
0
01 May 2025
pFedLVM: A Large Vision Model (LVM)-Driven and Latent Feature-Based Personalized Federated Learning Framework in Autonomous Driving
pFedLVM: A Large Vision Model (LVM)-Driven and Latent Feature-Based Personalized Federated Learning Framework in Autonomous Driving
Wei-Bin Kou
Qingfeng Lin
Ming Tang
Sheng Xu
Rongguang Ye
...
Shuai Wang
Guofa Li
Zhenyu Chen
Guangxu Zhu
Yik-Chung Wu
FedML
239
14
0
07 May 2024
1