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Object counting in remote sensing via selective spatial‐frequency pyramid network

https://doi.org/10.1002/spe.3287
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Is the edge really necessary for drone computing offloading? An experimental assessment in carrier‐grade 5G operator networks
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A novel forgery classification method based on multi‐scale feature capsule network in mobile edge computing
inferredcites →https://doi.org/10.1007/978-3-030-01228-1_45openalex-snapshot
Scale Aggregation Network for Accurate and Efficient Crowd Counting
inferredcites →https://doi.org/10.1007/978-3-319-46448-0_2openalex-snapshot
SSD: Single Shot MultiBox Detector
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A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning
inferredcites →https://doi.org/10.1007/s10489-023-04499-3openalex-snapshot
FPANet: feature pyramid attention network for crowd counting
inferredcites →https://doi.org/10.1007/s10586-022-03749-2openalex-snapshot
An attentive hierarchy ConvNet for crowd counting in smart city
inferredcites →https://doi.org/10.1007/s10586-023-03987-yopenalex-snapshot
A comprehensive analysis for crowd counting methodologies and algorithms in Internet of Things
inferredcites →https://doi.org/10.1007/s11036-023-02090-1openalex-snapshot
Dense Attention Fusion Network for Object Counting in IoT System
inferredcites →https://doi.org/10.1007/s11042-022-13664-8openalex-snapshot
Multiscale aggregation network via smooth inverse map for crowd counting
inferredcites →https://doi.org/10.1007/s12652-021-03377-5openalex-snapshot
An accurate car counting in aerial images based on convolutional neural networks
inferredcites →https://doi.org/10.1016/j.comnet.2015.12.023openalex-snapshot
Urban planning and building smart cities based on the Internet of Things using Big Data analytics
inferredcites →https://doi.org/10.1016/j.compmedimag.2020.101845openalex-snapshot
A deep learning approach to the screening of malaria infection: Automated and rapid cell counting, object detection and instance segmentation using Mask R-CNN
inferredcites →https://doi.org/10.1016/j.future.2023.05.013openalex-snapshot
Crowd counting in smart city via lightweight Ghost Attention Pyramid Network
inferredcites →https://doi.org/10.1016/j.jag.2022.102833openalex-snapshot
Road extraction in remote sensing data: A survey
inferredcites →https://doi.org/10.1016/j.neucom.2019.08.018openalex-snapshot
SCAR: Spatial-/channel-wise attention regression networks for crowd counting
inferredcites →https://doi.org/10.1016/j.neucom.2020.10.075openalex-snapshot
CentroidNetV2: A hybrid deep neural network for small-object segmentation and counting
inferredcites →https://doi.org/10.1089/big.2022.0039openalex-snapshot
Spatial-Frequency Attention Network for Crowd Counting
inferredcites →https://doi.org/10.1109/avss.2017.8078491openalex-snapshot
CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting
inferredcites →https://doi.org/10.1109/cac53003.2021.9728649openalex-snapshot
A Channel-aware Attention Network for Crowd Counting
inferredcites →https://doi.org/10.1109/comst.2017.2745201openalex-snapshot
A Survey on Mobile Edge Computing: The Communication Perspective
inferredcites →https://doi.org/10.1109/cvpr.2016.91openalex-snapshot
You Only Look Once: Unified, Real-Time Object Detection
inferredcites →https://doi.org/10.1109/cvpr.2018.00120openalex-snapshot
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
inferredcites →https://doi.org/10.1109/cvpr.2019.00839openalex-snapshot
Learning From Synthetic Data for Crowd Counting in the Wild
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00181openalex-snapshot
Learning in the Frequency Domain
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00476openalex-snapshot
Attention Scaling for Crowd Counting
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01900openalex-snapshot
Crowd Counting in the Frequency Domain
inferredcites →https://doi.org/10.1109/icassp40776.2020.9053690openalex-snapshot
Counting Dense Objects in Remote Sensing Images
inferredcites →https://doi.org/10.1109/iccv.2009.5459191openalex-snapshot
Bayesian Poisson regression for crowd counting
inferredcites →https://doi.org/10.1109/iccv.2015.372openalex-snapshot
COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation
inferredcites →https://doi.org/10.1109/iccv.2019.00358openalex-snapshot
Deep Residual Learning in the JPEG Transform Domain
inferredcites →https://doi.org/10.1109/iccv.2019.00624openalex-snapshot
Bayesian Loss for Crowd Count Estimation With Point Supervision
inferredcites →https://doi.org/10.1109/iccv48922.2021.00082openalex-snapshot
FcaNet: Frequency Channel Attention Networks
inferredcites →https://doi.org/10.1109/jiot.2023.3268226openalex-snapshot
Scale-Context Perceptive Network for Crowd Counting and Localization in Smart City System
inferredcites →https://doi.org/10.1109/tgrs.2020.3020555openalex-snapshot
Counting From Sky: A Large-Scale Data Set for Remote Sensing Object Counting and a Benchmark Method
inferredcites →https://doi.org/10.1109/tgrs.2022.3153946openalex-snapshot
PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote-Sensing Images
inferredcites →https://doi.org/10.1109/tip.2018.2875353openalex-snapshot
Divide and Count: Generic Object Counting by Image Divisions
inferredcites →https://doi.org/10.1109/tits.2022.3178848openalex-snapshot
Frequency Feature Pyramid Network With Global-Local Consistency Loss for Crowd-and-Vehicle Counting in Congested Scenes
inferredcites →https://doi.org/10.1109/tpami.2011.155openalex-snapshot
Pedestrian Detection: An Evaluation of the State of the Art
inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-snapshot
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
inferredcites →https://doi.org/10.1109/tpami.2018.2858826openalex-snapshot
Focal Loss for Dense Object Detection
inferredcites →https://doi.org/10.1109/wacv.2019.00211openalex-snapshot
Scale Pyramid Network for Crowd Counting
inferredcites →https://doi.org/10.1109/wacv48630.2021.00372openalex-snapshot
Coarse- and Fine-grained Attention Network with Background-aware Loss for Crowd Density Map Estimation
inferredcites →https://doi.org/10.1155/2021/5551976openalex-snapshot
Towards AI-Based Traffic Counting System with Edge Computing
inferredcites →https://doi.org/10.3390/rs14164026openalex-snapshot
Global Multi-Scale Information Fusion for Multi-Class Object Counting in Remote Sensing Images
inferredcites →https://doi.org/10.3390/rs14246363openalex-snapshot
Object Counting in Remote Sensing via Triple Attention and Scale-Aware Network
inferredcites →https://doi.org/10.3390/rs9010022openalex-snapshot
Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/spe.3362openalex-snapshot
Special Issue on “Ensuring security for artificial intelligence applications in mobile edge computing software systems”
inferred← citeshttps://doi.org/10.1016/j.inffus.2024.102537openalex-snapshot
Towards zero-shot object counting via deep spatial prior cross-modality fusion
inferred← citeshttps://doi.org/10.1016/j.inffus.2025.103837openalex-snapshot
Towards generalizable object counting via dual-prompted cross-modality fusion network
inferred← citeshttps://doi.org/10.1088/1361-6501/ada6f1openalex-snapshot
Efficient vehicular counting via privacy-aware aggregation network
inferred← citeshttps://doi.org/10.1088/1361-6501/adea12openalex-snapshot
Crowd counting via lightweight neural networks: a literature review
inferred← citeshttps://doi.org/10.1109/cac67268.2025.11487391openalex-snapshot
Towards Object Counting by Efficient Scale Recognition Network
inferred← citeshttps://doi.org/10.1109/ictai66417.2025.00099openalex-snapshot
SAM-CLIP Counting Net: An Arbitrary-Shot Framework for Class-Agnostic Object Counting
inferred← citeshttps://doi.org/10.1109/isoirs65690.2025.11168067openalex-snapshot
Efficient Crowd Counting Via Region-Aware Enhancement Network
inferred← citeshttps://doi.org/10.1109/tce.2025.3557449openalex-snapshot
Dual Backbone Multi-Attention Hierarchical Fusion and Feature Enhancement Network for Crowd Counting
inferred← citeshttps://doi.org/10.1109/tcsvt.2025.3588406openalex-snapshot
UP-Person: Unified Parameter-Efficient Transfer Learning for Text-Based Person Retrieval
inferred← citeshttps://doi.org/10.1117/1.jei.33.4.043002openalex-snapshot
SDANet: scale-deformation awareness network for crowd counting
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