SOTAVerified

Object Counting

The goal of Object Counting task is to count the number of object instances in a single image or video sequence. It has many real-world applications such as traffic flow monitoring, crowdedness estimation, and product counting.

Source: Learning to Count Objects with Few Exemplar Annotations

Papers

Showing 76–100 of 158 papers

TitleStatusHype
An Image Processing based Object Counting Approach for Machine Vision Application—0
AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis—0
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?—0
A Statistical Method for Object Counting—0
A Survey on Class-Agnostic Counting: Advancements from Reference-Based to Open-World Text-Guided Approaches—0
Boundary Attention Constrained Zero-Shot Layout-To-Image Generation—0
ChatGPT and general-purpose AI count fruits in pictures surprisingly well—0
CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection—0
Contextual Hourglass Networks for Segmentation and Density Estimation—0
Counting and Locating High-Density Objects Using Convolutional Neural Network—0
Counting dense objects in remote sensing images—0
Counting Objects in a Robotic Hand—0
Counting Stacked Objects from Multi-View Images—0
Counting the uncountable: deep semantic density estimation from Space—0
Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review—0
DeepSetNet: Predicting Sets with Deep Neural Networks—0
Density Map Distillation for Incremental Object Counting—0
Diffusion-based Data Augmentation for Object Counting Problems—0
Dilated-Scale-Aware Attention ConvNet For Multi-Class Object Counting—0
Distortion-Adaptive Grape Bunch Counting for Omnidirectional Images—0
Do Object Detection Localization Errors Affect Human Performance and Trust?—0
Drone-based Object Counting by Spatially Regularized Regional Proposal Network—0
Dynamic Conditional Networks for Few-Shot Learning—0
Effectiveness Assessment of Recent Large Vision-Language Models—0
Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale Benchmark—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FamNetMAE(test)22.08—Unverified
2Omnicount (Open vocabulary, multi-label, without training)MAE(test)18.63—Unverified
3RCCMAE(test)17.12—Unverified
4Counting-DETRMAE(test)16.79—Unverified
5CounTX (uses text descriptions instead of visual exemplars)MAE(test)15.88—Unverified
6LaoNetMAE(test)15.78—Unverified
7BMNet+MAE(test)14.62—Unverified
8SAFECountMAE(test)14.32—Unverified
9GCA-SUNMAE(test)14—Unverified
10SPDCNMAE(test)13.51—Unverified
#ModelMetricClaimedVerifiedStatus
1YOLO (2016)MAE156—Unverified
2YOLO9000opt (2017)MAE130.4—Unverified
3Faster R-CNN (2015)MAE39.88—Unverified
4RetinaNet (2018)MAE24.58—Unverified
5LPN Counting (2017)MAE22.76—Unverified
6One-Look Regression (2016)MAE21.88—Unverified
7RetinaNet (2018)MAE16.62—Unverified
8CounTX (uses arbitrary text input to specify object to count, used "the cars" for CARPK)MAE8.13—Unverified
9Soft-IoU + EM-Merger unitMAE6.77—Unverified
10VLCounterMAE6.46—Unverified
#ModelMetricClaimedVerifiedStatus
1Fast-RCNNm-reIRMSE-nz0.85—Unverified
2glance-noft-2Lm-reIRMSE-nz0.73—Unverified
3LC-PSPNetm-reIRMSE-nz0.7—Unverified
4Seq-sub-ft-3x3m-reIRMSE-nz0.68—Unverified
5ensm-reIRMSE-nz0.65—Unverified
6LC-ResFCNm-reIRMSE-nz0.61—Unverified
7Supervised Density Mapm-reIRMSE-nz0.61—Unverified
8OmnicountmRMSE0—Unverified
#ModelMetricClaimedVerifiedStatus
1Aso-sub-ft-3x3m-reIRMSE0.24—Unverified
2glance-ft-2Lm-reIRMSE0.23—Unverified
3Fast-RCNNm-reIRMSE0.2—Unverified
4LC-ResFCNm-reIRMSE0.19—Unverified
5Supervised Density Mapm-reIRMSE0.18—Unverified
6ensm-reIRMSE0.18—Unverified
7Seq-sub-ft-3x3m-reIRMSE0.18—Unverified
#ModelMetricClaimedVerifiedStatus
1SMoLA-PaLI-X SpecialistAccuracy77.1—Unverified
2PaLI-X-VPDAccuracy76.6—Unverified
3SMoLA-PaLI-X Generalist (0 shot)Accuracy70.7—Unverified
4MoVie-ResNeXtAccuracy56.8—Unverified
5RCNAccuracy56.2—Unverified
6MoVieAccuracy54.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SMoLA-PaLI-X SpecialistAccuracy86.3—Unverified
2PaLI-X-VPDAccuracy86.2—Unverified
3SMoLA-PaLI-X Generalist (0 shot)Accuracy83.3—Unverified
4MoVie-ResNeXtAccuracy74.9—Unverified
5RCNAccuracy71.8—Unverified
6MoVieAccuracy70.8—Unverified
#ModelMetricClaimedVerifiedStatus
1MoVie-ResNeXtAccuracy64—Unverified
2MoVieAccuracy61.2—Unverified
3RCNAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CEOESmRMSE0.42—Unverified
2ILCmRMSE0.29—Unverified
3TFOCmRMSE0.01—Unverified
#ModelMetricClaimedVerifiedStatus
1OmnicountmRMSE0—Unverified
#ModelMetricClaimedVerifiedStatus
1GauNet (ResNet-50)MAE2.1—Unverified