Object Counting for Remote-Sensing Images via Adaptive Density Map-Assisted Learning

Author:

Ding Guanchen1ORCID,Cui Mingpeng1,Yang Daiqin1ORCID,Wang Tao2,Wang Sihan2,Zhang Yunfei2ORCID

Affiliation:

1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

2. Tencent, Shenzhen, China

Funder

National Natural Science Foundation of China

Tencent

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Reference54 articles.

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2. Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

3. BERT: Pre-training of deep bidirectional transformers for language understanding;devlin;arXiv 1810 04805,2018

4. Attention is all you need;vaswani;Proc Adv Neural Inf Process Syst,2017

5. PSGCNet: A pyramidal scale and global context guided network for dense object counting in remote sensing images;gao;arXiv 2012 03597,2020

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1. Balanced Density Regression Network for Remote Sensing Object Counting;IEEE Transactions on Geoscience and Remote Sensing;2024

2. DOPNet: Dense Object Prediction Network for Multiclass Object Counting and Localization in Remote Sensing Images;IEEE Transactions on Geoscience and Remote Sensing;2024

3. NWPU-MOC: A Benchmark for Fine-Grained Multicategory Object Counting in Aerial Images;IEEE Transactions on Geoscience and Remote Sensing;2024

4. Hierarchical Kernel Interaction Network for Remote Sensing Object Counting;IEEE Transactions on Geoscience and Remote Sensing;2024

5. Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images;IEEE Transactions on Neural Networks and Learning Systems;2024

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