CASIA OpenIR  > 智能感知与计算研究中心
Thesis Advisor张兆翔
Degree Grantor中国科学院大学
Place of Conferral北京
Degree Discipline计算机技术



Other Abstract

Weakly-supervised object localization is a kind of special problem in visual localization, because it is usually only provided with image-level labels in the task, which is different from fully-supervised object localization, the latter task could have both image-level labels and bounding box annotations as supervision information. Thus, with less supervision, weakly-supervised object localization is more difficult than the fully-supervised one. However, less supervision also means less dependency on human-labour to annotate the datasets, which can save large amounts of human-labour and money, as well as improve the adaptability of weakly-supervised object localization algorithms and frameworks on more datasets. Recently, weakly-supervised object localization became a hot research topic and attracted much attention. Therefore, we propose a deep reinforcement learning approach to achieve weakly-supervised object localization by cutting background, and also design a novel refinement method to further improve the localization performance, which achieves good results on popular datasets. The main contributions of this work can be summarized as follows:
    We propose a localization method by cutting object background and model the cutting process as a Markov decision process, which can be solved by the designed deep reinforcement learning algorithm. The proposed method complies more with human visual mechanism and has higher efficiency.
    We propose a localization refinement method by re-organizing the convolution feature maps. This method integrates the location information in the feature maps and takes it as complementarity for the deep reinforcement learning framework, with localization performance improved significantly.
    We proposed a localization framework that can easily be embedded into existing convolutional neural networks to realize localization and classification simultaneously.

In the end, we summarized the key issues and coping strategies in the proposed methods as well as the research directions that need to be explored in the future.

Document Type学位论文
Recommended Citation
GB/T 7714
郑武. 基于深度强化学习裁剪物体背景的弱监督物体定位[D]. 北京. 中国科学院大学,2019.
Files in This Item:
File Name/Size DocType Version Access License
Thesis.pdf(3920KB)学位论文 开放获取CC BY-NC-SAApplication Full Text
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[郑武]'s Articles
Baidu academic
Similar articles in Baidu academic
[郑武]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[郑武]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.