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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–150 of 158 papers

TitleStatusHype
OmniCount: Multi-label Object Counting with Semantic-Geometric Priors—0
Omni-SMoLA: Boosting Generalist Multimodal Models with Soft Mixture of Low-rank Experts—0
OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Models—0
On the Promises and Challenges of Multimodal Foundation Models for Geographical, Environmental, Agricultural, and Urban Planning Applications—0
Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale Benchmark—0
Overconfidence is Key: Verbalized Uncertainty Evaluation in Large Language and Vision-Language Models—0
People, Penguins and Petri Dishes: Adapting Object Counting Models To New Visual Domains And Object Types Without Forgetting—0
Tolerating Annotation Displacement in Dense Object Counting via Point Annotation Probability Map—0
Effectiveness Assessment of Recent Large Vision-Language Models—0
Dynamic Conditional Networks for Few-Shot Learning—0
Drone-based Object Counting by Spatially Regularized Regional Proposal Network—0
Real-Time Object Detection in Occluded Environment with Background Cluttering Effects Using Deep Learning—0
Do Object Detection Localization Errors Affect Human Performance and Trust?—0
Distortion-Adaptive Grape Bunch Counting for Omnidirectional Images—0
Dilated-Scale-Aware Attention ConvNet For Multi-Class Object Counting—0
Diffusion-based Data Augmentation for Object Counting Problems—0
Reverse Perspective Network for Perspective-Aware Object Counting—0
Why Vision Language Models Struggle with Visual Arithmetic? Towards Enhanced Chart and Geometry Understanding—0
Density Map Distillation for Incremental Object Counting—0
DeepSetNet: Predicting Sets with Deep Neural Networks—0
RSVQA: Visual Question Answering for Remote Sensing Data—0
Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review—0
Counting the uncountable: deep semantic density estimation from Space—0
Segmentação e contagem de troncos de madeira utilizando deep learning e processamento de imagens—0
Counting Stacked Objects from Multi-View Images—0
Counting Objects in a Robotic Hand—0
Counting dense objects in remote sensing images—0
SIMCO: SIMilarity-based object COunting—0
Single Image Object Counting and Localizing using Active-Learning—0
Counting and Locating High-Density Objects Using Convolutional Neural Network—0
Contextual Hourglass Networks for Segmentation and Density Estimation—0
CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection—0
ChatGPT and general-purpose AI count fruits in pictures surprisingly well—0
A Few-Shot Sequential Approach for Object Counting—0
0-1 phase transitions in sparse spiked matrix estimation—0
TFCounter:Polishing Gems for Training-Free Object Counting—0
Boundary Attention Constrained Zero-Shot Layout-To-Image Generation—0
Towards Locally Consistent Object Counting with Constrained Multi-stage Convolutional Neural Networks—0
A Density-Guided Temporal Attention Transformer for Indiscernible Object Counting in Underwater Video—0
A-CCNN: adaptive ccnn for density estimation and crowd counting—0
A Survey on Class-Agnostic Counting: Advancements from Reference-Based to Open-World Text-Guided Approaches—0
T-Rex: Counting by Visual Prompting—0
TS4Net: Two-Stage Sample Selective Strategy for Rotating Object Detection—0
Understanding the Ability of Deep Neural Networks to Count Connected Components in Images—0
A Statistical Method for Object Counting—0
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?—0
AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis—0
A Causal Lens for Evaluating Faithfulness Metrics—0
Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models—0
An Image Processing based Object Counting Approach for Machine Vision Application—0
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All datasetsFSC147CARPKPascal VOC 2007 count-testCOCO count-testTallyQA-ComplexTallyQA-SimpleHowMany-QAPASCAL VOCOmnicount-191TRANCOS

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
6Supervised Density Mapm-reIRMSE-nz0.61—Unverified
7LC-ResFCNm-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
5Seq-sub-ft-3x3m-reIRMSE0.18—Unverified
6Supervised Density Mapm-reIRMSE0.18—Unverified
7ensm-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