NAS-Kernel: Learning Suitable Gaussian Kernel for Remote-Sensing Object Counting-Reference-Cited by-同舟云学术

NAS-Kernel: Learning Suitable Gaussian Kernel for Remote-Sensing Object Counting

Published:2023 Issue: Volume:20 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Zhao Liangliang¹^ORCID,Gao Junyu²^ORCID,Li Xuelong¹^ORCID

Affiliation:

1. School of Artificial Intelligence, and the OPtics and ElectroNics (iOPEN), School of Computer Science, Northwestern Polytechnical University, Xi’an, China

2. School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an, China

Funder

National Key Research and Development Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/10034981/10247016.pdf?arnumber=10247016

Reference22 articles.

1. Global Multi-Scale Information Fusion for Multi-Class Object Counting in Remote Sensing Images

2. Kernel-Based Density Map Generation for Dense Object Counting

3. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

4. Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation

5. Dual path multi-scale fusion networks with attention for crowd counting;zhu;arXiv 1902 01115,2019

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