Visual Relationship Detection: A Survey-Reference-Cited by-同舟云学术

Visual Relationship Detection: A Survey

Published:2022-08 Issue:8 Volume:52 Page:8453-8466
ISSN:2168-2267
Container-title:IEEE Transactions on Cybernetics
language:
Short-container-title:IEEE Trans. Cybern.

Author:

Cheng Jun¹^ORCID,Wang Lei¹^ORCID,Wu Jiaji²^ORCID,Hu Xiping³,Jeon Gwanggil⁴^ORCID,Tao Dacheng⁵^ORCID,Zhou Mengchu⁶^ORCID

Affiliation:

1. Shenzhen Institute of Advanced Technology, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, and Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Chinese Academy of Sciences, Shenzhen, China

2. School of Electronic Engineering, Xidian University, Xi’an, China

3. School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China

4. Department of Embedded Systems Engineering, Incheon National University, Incheon, South Korea

5. JD Explore Academy, JD.com, Beijing, China

6. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USA

Funder

National Natural Science Foundation of China

Basic and Applied Basic Research Foundation of Guangdong Province

CAS Key Technology Talent Program, Colleges and Universities Key Laboratory of Intelligent Integrated Automation

CAS Key Laboratory of Human–Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences

Guangdong-Hong Kong-Macao Joint Laboratory of Human–Machine Intelligence-Synergy Systems

3D Digital Media Technology Engineering Laboratory

Shenzhen Technology Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221036/9833007/09693341.pdf?arnumber=9693341

Reference101 articles.

1. Image Style Transfer Using Convolutional Neural Networks

2. Describing Video With Attention-Based Bidirectional LSTM

3. Deep Attention-Based Spatially Recursive Networks for Fine-Grained Visual Recognition

4. Deep Residual Learning for Image Recognition

5. Going deeper with convolutions

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