LID 2020: The Learning from Imperfect Data Challenge Results

Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotated datasets. The purpose of the Learning from Imperfect Data (LID) workshop is to inspire and facilitate the research in...

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Veröffentlicht in:	arXiv.org 2020-10
Hauptverfasser:	Wei, Yunchao, Zheng, Shuai, Ming-Ming, Cheng, Zhao, Hang, Wang, Liwei, Ding, Errui, Yang, Yi, Torralba, Antonio, Liu, Ting, Sun, Guolei, Wang, Wenguan, Luc Van Gool, Bae, Wonho, Noh, Junhyug, Seo, Jinhwan, Kim, Gunhee, Zhao, Hao, Lu, Ming, Yao, Anbang, Guo, Yiwen, Chen, Yurong, Zhang, Li, Tan, Chuangchuang, Ruan, Tao, Gu, Guanghua, Wei, Shikui, Zhao, Yao, Dobko, Mariia, Viniavskyi, Ostap, Oles Dobosevych, Wang, Zhendong, Chen, Zhenyuan, Chen, Gong, Yan, Huanqing, He, Jun
Format:	Artikel
Sprache:	eng
Schlagworte:	Evaluation Image annotation Image segmentation Industrial applications Machine learning Object recognition Pixels Semantic segmentation Semantics Training Workshops
Online-Zugang:	Volltext
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