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

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Showing 101–150 of 158 papers

TitleStatusHype
Expanding Zero-Shot Object Counting with Rich Prompts—0
Fast-moving object counting with an event camera—0
FocalCount: Towards Class-Count Imbalance in Class-Agnostic Counting—0
Hierarchical Alignment-enhanced Adaptive Grounding Network for Generalized Referring Expression Comprehension—0
Improved Counting and Localization from Density Maps for Object Detection in 2D and 3D Microscopy Imaging—0
Interactive Class-Agnostic Object Counting—0
Detection-Driven Object Count Optimization for Text-to-Image Diffusion Models—0
Learning from Pseudo-labeled Segmentation for Multi-Class Object Counting—0
Learning Short-Cut Connections for Object Counting—0
Learning-to-Count by Learning-to-Rank: Weakly Supervised Object Counting & Localization Using Only Pairwise Image Rankings—0
Learning to Count Grave Sites for Cemetery Observation Models With Satellite Imagery—0
Learning To Count Objects in Images—0
Learning to Count Objects with Few Exemplar Annotations—0
Learning What NOT to Count—0
Low-Power Object Counting with Hierarchical Neural Networks—0
Mamba-MOC: A Multicategory Remote Object Counting via State Space Model—0
Marmot: Multi-Agent Reasoning for Multi-Object Self-Correcting in Improving Image-Text Alignment—0
MATHGLANCE: Multimodal Large Language Models Do Not Know Where to Look in Mathematical Diagrams—0
Mutually-Aware Feature Learning for Few-Shot Object Counting—0
Shifted Autoencoders for Point Annotation Restoration in Object Counting—0
Object counting from aerial remote sensing images: application to wildlife and marine mammals—0
Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images—0
Object Counting: You Only Need to Look at One—0
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
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
Real-Time Object Detection in Occluded Environment with Background Cluttering Effects Using Deep Learning—0
TFCounter:Polishing Gems for Training-Free Object Counting—0
Towards Locally Consistent Object Counting with Constrained Multi-stage Convolutional Neural Networks—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
Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models—0
Why Vision Language Models Struggle with Visual Arithmetic? Towards Enhanced Chart and Geometry Understanding—0
0-1 phase transitions in sparse spiked matrix estimation—0
Towards perspective-free object counting with deep learningCode0
Improving Contrastive Learning for Referring Expression CountingCode0
AFreeCA: Annotation-Free Counting for AllCode0
Class-Agnostic CountingCode0
Car Object Counting and Position Estimation via Extension of the CLIP-EBC FrameworkCode0
Griffon v2: Advancing Multimodal Perception with High-Resolution Scaling and Visual-Language Co-ReferringCode0
GCA-SUNet: A Gated Context-Aware Swin-UNet for Exemplar-Free CountingCode0
Domain Randomization for Object CountingCode0
Vision Transformers for Weakly-Supervised Microorganism EnumerationCode0
A Unified Object Counting Network with Object Occupation PriorCode0
Dense Center-Direction Regression for Object Counting and Localization with Point SupervisionCode0
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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
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