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 1–10 of 158 papers

TitleStatusHype
Car Object Counting and Position Estimation via Extension of the CLIP-EBC FrameworkCode0
OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Models—0
Improving Contrastive Learning for Referring Expression CountingCode0
Expanding Zero-Shot Object Counting with Rich Prompts—0
InstructSAM: A Training-Free Framework for Instruction-Oriented Remote Sensing Object RecognitionCode2
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?—0
VisionReasoner: Unified Visual Perception and Reasoning via Reinforcement LearningCode4
Learning What NOT to Count—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
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All datasetsFSC147CARPKPascal VOC 2007 count-testCOCO count-testTallyQA-ComplexTallyQA-SimpleHowMany-QAPASCAL VOCOmnicount-191TRANCOS

Benchmark Results

#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