Deep Learning for Weakly-Supervised Object Detection and Localization: A Survey
Authors:
- Feifei ShaoFSFeifei Shao
- Long ChenLCLong Chen
- Jian ShaoJSJian Shao
- Wei JiWJWei Ji
- Shaoning XiaoSXShaoning Xiao
- Lu YeLYLu Ye
- Yueting ZhuangYZYueting Zhuang
- Jun XiaoJXJun Xiao
- 29 January 2022
- journal article
- research article
- Published by Elsevier in Neurocomputing
- Vol. 496, 192-207
Abstract
No abstract availableKeywords
All Related Versions
Funding Information
- Natural Science Foundation of Zhejiang Province (LR19F020002, LZ17F020001)
- Fundamental Research Funds for the Central Universities
- China Knowledge Centre for Engineering Sciences and Technology
- Fundamental Research Funds for the Central Universities
- National Natural Science Foundation of China (61976185, U19B2043)
This publication has 11 references indexed in Scilit:
- Natural Language Video Localization with Learnable Moment Proposals2021
- Enabling Deep Residual Networks for Weakly Supervised Object Detection2020
- Rethinking the Bottom-Up Framework for Query-Based Video LocalizationProceedings of the AAAI Conference on Artificial Intelligence, 2020
- DEBUG: A Dense Bottom-Up Grounding Approach for Natural Language Video Localization2019
- TS$^{2}$C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection2018
- Mask R-CNN2017
- SSD: Single Shot MultiBox Detector2016
- Weakly Supervised Deep Detection Networks2016
- Fast R-CNN2015
- Microsoft COCO: Common Objects in Context2014