Controllable Model Compression for Roadside Camera Depth Estimation-Reference-Cited by-同舟云学术

Controllable Model Compression for Roadside Camera Depth Estimation

Published:2022 Issue: Volume: Page:1-8
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Ople Jose Jaena Mari¹,Chen Shang-Fu¹,Chen Yung-Yao¹,Hua Kai-Lung¹,Hijji Mohammad²^ORCID,Yang Po³^ORCID,Muhammad Khan⁴^ORCID

Affiliation:

1. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan.

2. Industrial Innovation and Robotic Center (IIRC), University of Tabuk, Tabuk 47711, Saudi Arabia.

3. Department of Computer Science, The University of Sheffield, Sheffield S1 4DP, U.K..

4. Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Applied Artificial Intelligence, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Republic of Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/4358928/09774972.pdf?arnumber=9774972

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1. Synthetic Data Enhancement and Network Compression Technology of Monocular Depth Estimation for Real-Time Autonomous Driving System;Sensors;2024-06-28

2. GazeVAE: Gaze Visual Attention Estimator;2024 IEEE International Conference on Consumer Electronics (ICCE);2024-01-06

3. End-to-End Variable-Rate Learning-Based Depth Compression Guided by Deep Correlation Features;Journal of Signal Processing Systems;2024-01

4. Toward Efficient and Secure Object Detection With Sparse Federated Training Over Internet of Vehicles;IEEE Transactions on Intelligent Transportation Systems;2024