Improving Multiple Dense Prediction Performances by Exploiting Inter-Task Synergies for Neuromorphic Vision Sensors-Reference-Cited by-同舟云学术

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Improving Multiple Dense Prediction Performances by Exploiting Inter-Task Synergies for Neuromorphic Vision Sensors

Published:2024-08-01 Issue:15 Volume:24 Page:25006-25018
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Zhang Tian¹^ORCID,Li Zhaokun²,Su Jianbo¹^ORCID,Fang Jingjing³

Affiliation:

1. Department of Automation, Research Center of Intelligent Robotics, Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China

2. State Key Laboratory of Integrated Service Network, School of Telecommunications Engineering, Xidian University, Xi’an, China

3. Naval Medical Centre, Shanghai, China

Funder

Shanghai Cross-Disciplinary Research Fund

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/7361/10617804/10557546.pdf?arnumber=10557546

Reference56 articles.

1. EV-SegNet: Semantic Segmentation for Event-Based Cameras

2. Accurate and efficient event-based semantic segmentation using adaptive spiking encoder–decoder network;Zhang;arXiv:2304.11857,2023

3. ESS: Learning Event-Based Semantic Segmentation from Still Images

4. Event-based monocular dense depth estimation with recurrent transformers;Liu;arXiv:2212.02791,2022

5. Learning Monocular Dense Depth from Events

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