Deep active learning for object detection-Reference-Cited by-同舟云学术

Deep active learning for object detection

Published:2021-11 Issue: Volume:579 Page:418-433
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Li Ying,Fan Binbin,Zhang Weiping,Ding Weiping,Yin Jianwei

Funder

Natural Science Foundation of Jiangsu Province

Qinglan Project of Jiangsu Province of China

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference31 articles.

1. Faster R-CNN: towards real-time object detection with region proposal networks;Ren,2015

2. You only look once: Unified, real-time object detection;Redmon,2016

3. Focal loss for dense object detection;Lin,2017

4. P. Ren, Y. Xiao, X. Chang, P. Huang, Z. Li, X. Chen, X. Wang, A survey of deep active learning (2020). arXiv:2009.00236.

5. Active learning for convolutional neural networks: A core-set approach;Sener,2018

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