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 150 of 158 papers

TitleStatusHype
VisionReasoner: Unified Visual Perception and Reasoning via Reinforcement LearningCode4
DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation ModelsCode3
CountGD: Multi-Modal Open-World CountingCode3
Roboflow 100: A Rich, Multi-Domain Object Detection BenchmarkCode2
RS-Agent: Automating Remote Sensing Tasks through Intelligent AgentCode2
GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial TasksCode2
CNN-based Density Estimation and Crowd Counting: A SurveyCode2
InstructSAM: A Training-Free Framework for Instruction-Oriented Remote Sensing Object RecognitionCode2
RemoteCLIP: A Vision Language Foundation Model for Remote SensingCode2
Self-Supervised Learning from Images with a Joint-Embedding Predictive ArchitectureCode2
Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and AnalysisCode2
A Novel Unified Architecture for Low-Shot Counting by Detection and SegmentationCode2
DAVE -- A Detect-and-Verify Paradigm for Low-Shot CountingCode2
Point Segment and Count: A Generalized Framework for Object CountingCode2
Point, Segment and Count: A Generalized Framework for Object CountingCode1
NWPU-MOC: A Benchmark for Fine-grained Multi-category Object Counting in Aerial ImagesCode1
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel SynthesisCode1
Learning To Count EverythingCode1
Learning to Count without AnnotationsCode1
Semantic Generative Augmentations for Few-Shot CountingCode1
STEERER: Resolving Scale Variations for Counting and Localization via Selective Inheritance LearningCode1
RGB-D Indiscernible Object Counting in Underwater ScenesCode1
PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote Sensing ImagesCode1
Few-shot Object Counting with Similarity-Aware Feature EnhancementCode1
Learning to Count Anything: Reference-less Class-agnostic Counting with Weak SupervisionCode1
Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-CaseCode1
A Low-Shot Object Counting Network With Iterative Prototype AdaptationCode1
SAVE: Self-Attention on Visual Embedding for Zero-Shot Generic Object CountingCode1
Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic CountingCode1
Open-world Text-specified Object CountingCode1
Rethinking Spatial Invariance of Convolutional Networks for Object CountingCode1
Scale-Prior Deformable Convolution for Exemplar-Guided Class-Agnostic CountingCode1
Can SAM Count Anything? An Empirical Study on SAM CountingCode1
Counting with Adaptive Auxiliary LearningCode1
Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd CountingCode1
Few-shot Object Counting and DetectionCode1
CounTR: Transformer-based Generalised Visual CountingCode1
Few-shot Object LocalizationCode1
Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware LearningCode1
Automating cell counting in fluorescent microscopy through deep learning with c-ResUnetCode1
From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object CountingCode1
Counting from Sky: A Large-scale Dataset for Remote Sensing Object Counting and A Benchmark MethodCode1
An Accurate Car Counting in Aerial Images Based on Convolutional Neural NetworksCode1
Heatmap-based Object Detection and Tracking with a Fully Convolutional Neural NetworkCode1
EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question AnsweringCode1
IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance SamplingCode1
Class-agnostic-Few-shot-Object-CountingCode1
Mind the Prompt: A Novel Benchmark for Prompt-based Class-Agnostic CountingCode1
CLIP-Count: Towards Text-Guided Zero-Shot Object CountingCode1
Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORTCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FamNetMAE(test)22.08Unverified
2Omnicount (Open vocabulary, multi-label, without training)MAE(test)18.63Unverified
3RCCMAE(test)17.12Unverified
4Counting-DETRMAE(test)16.79Unverified
5CounTX (uses text descriptions instead of visual exemplars)MAE(test)15.88Unverified
6LaoNetMAE(test)15.78Unverified
7BMNet+MAE(test)14.62Unverified
8SAFECountMAE(test)14.32Unverified
9GCA-SUNMAE(test)14Unverified
10SPDCNMAE(test)13.51Unverified
#ModelMetricClaimedVerifiedStatus
1YOLO (2016)MAE156Unverified
2YOLO9000opt (2017)MAE130.4Unverified
3Faster R-CNN (2015)MAE39.88Unverified
4RetinaNet (2018)MAE24.58Unverified
5LPN Counting (2017)MAE22.76Unverified
6One-Look Regression (2016)MAE21.88Unverified
7RetinaNet (2018)MAE16.62Unverified
8CounTX (uses arbitrary text input to specify object to count, used "the cars" for CARPK)MAE8.13Unverified
9Soft-IoU + EM-Merger unitMAE6.77Unverified
10VLCounterMAE6.46Unverified
#ModelMetricClaimedVerifiedStatus
1Fast-RCNNm-reIRMSE-nz0.85Unverified
2glance-noft-2Lm-reIRMSE-nz0.73Unverified
3LC-PSPNetm-reIRMSE-nz0.7Unverified
4Seq-sub-ft-3x3m-reIRMSE-nz0.68Unverified
5ensm-reIRMSE-nz0.65Unverified
6Supervised Density Mapm-reIRMSE-nz0.61Unverified
7LC-ResFCNm-reIRMSE-nz0.61Unverified
8OmnicountmRMSE0Unverified
#ModelMetricClaimedVerifiedStatus
1Aso-sub-ft-3x3m-reIRMSE0.24Unverified
2glance-ft-2Lm-reIRMSE0.23Unverified
3Fast-RCNNm-reIRMSE0.2Unverified
4LC-ResFCNm-reIRMSE0.19Unverified
5Seq-sub-ft-3x3m-reIRMSE0.18Unverified
6Supervised Density Mapm-reIRMSE0.18Unverified
7ensm-reIRMSE0.18Unverified
#ModelMetricClaimedVerifiedStatus
1SMoLA-PaLI-X SpecialistAccuracy77.1Unverified
2PaLI-X-VPDAccuracy76.6Unverified
3SMoLA-PaLI-X Generalist (0 shot)Accuracy70.7Unverified
4MoVie-ResNeXtAccuracy56.8Unverified
5RCNAccuracy56.2Unverified
6MoVieAccuracy54.1Unverified
#ModelMetricClaimedVerifiedStatus
1SMoLA-PaLI-X SpecialistAccuracy86.3Unverified
2PaLI-X-VPDAccuracy86.2Unverified
3SMoLA-PaLI-X Generalist (0 shot)Accuracy83.3Unverified
4MoVie-ResNeXtAccuracy74.9Unverified
5RCNAccuracy71.8Unverified
6MoVieAccuracy70.8Unverified
#ModelMetricClaimedVerifiedStatus
1MoVie-ResNeXtAccuracy64Unverified
2MoVieAccuracy61.2Unverified
3RCNAccuracy60.3Unverified
#ModelMetricClaimedVerifiedStatus
1CEOESmRMSE0.42Unverified
2ILCmRMSE0.29Unverified
3TFOCmRMSE0.01Unverified
#ModelMetricClaimedVerifiedStatus
1OmnicountmRMSE0Unverified
#ModelMetricClaimedVerifiedStatus
1GauNet (ResNet-50)MAE2.1Unverified