Selecting High-Quality Proposals for Weakly Supervised Object Detection With Bottom-Up Aggregated Attention and Phase-Aware Loss-Reference-Cited by-同舟云学术

Selecting High-Quality Proposals for Weakly Supervised Object Detection With Bottom-Up Aggregated Attention and Phase-Aware Loss

Published:2023 Issue: Volume:32 Page:682-693
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Wu Zhihao¹^ORCID,Liu Chengliang¹^ORCID,Wen Jie¹^ORCID,Xu Yong¹^ORCID,Yang Jian²,Li Xuelong³^ORCID

Affiliation:

1. Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen, China

2. PCA Laboratory, Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, Nanjing University of Science and Technology, Nanjing, China

3. Center for OPTical IMagery Analysis and Learning (OPTIMAL), School of Computer Science, Northwestern Polytechnical University, Xi’an, China

Funder

Establishment of Key Laboratory of Shenzhen Science and Technology Innovation Committee

Key Program of Technology Research from Shenzhen Science and Technology Innovation Committee

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Graphics and Computer-Aided Design,Software

Link

http://xplorestaging.ieee.org/ielx7/83/9991910/10003247.pdf?arnumber=10003247

Reference60 articles.

1. The Pascal Visual Object Classes (VOC) Challenge

2. Towards Precise End-to-End Weakly Supervised Object Detection Network

3. Deep Object Detection with Example Attribute Based Prediction Modulation

4. Objectness Consistent Representation for Weakly Supervised Object Detection

5. Objects365: A Large-Scale, High-Quality Dataset for Object Detection

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