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 1–10 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
Point Segment and Count: A Generalized Framework for Object CountingCode2
Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and AnalysisCode2
CNN-based Density Estimation and Crowd Counting: A SurveyCode2
GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial TasksCode2
A Novel Unified Architecture for Low-Shot Counting by Detection and SegmentationCode2
DAVE -- A Detect-and-Verify Paradigm for Low-Shot CountingCode2
InstructSAM: A Training-Free Framework for Instruction-Oriented Remote Sensing Object RecognitionCode2
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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
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