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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 126–150 of 158 papers

Title	Date	Tasks	Status
Dilated-Scale-Aware Attention ConvNet For Multi-Class Object Counting	Dec 15, 2020	ObjectObject Counting	—Unverified
Learning to Count Grave Sites for Cemetery Observation Models With Satellite Imagery	Sep 22, 2020	Object Counting	—Unverified
Distortion-Adaptive Grape Bunch Counting for Omnidirectional Images	Aug 28, 2020	Data AugmentationObject Counting	—Unverified
A Few-Shot Sequential Approach for Object Counting	Jul 3, 2020	ObjectObject Counting	—Unverified
Low-Power Object Counting with Hierarchical Neural Networks	Jul 2, 2020	ObjectObject Counting	—Unverified
Reverse Perspective Network for Perspective-Aware Object Counting	Jun 1, 2020	ObjectObject Counting	—Unverified
MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond	Apr 24, 2020	Object CountingQuestion Answering	CodeCode Available
RSVQA: Visual Question Answering for Remote Sensing Data	Mar 16, 2020	Land Cover ClassificationObject Counting	—Unverified
Counting dense objects in remote sensing images	Feb 14, 2020	Object Counting	—Unverified
Towards Partial Supervision for Generic Object Counting in Natural Scenes	Dec 13, 2019	image-classificationImage Classification	CodeCode Available
0-1 phase transitions in sparse spiked matrix estimation	Nov 12, 2019	Image RestorationObject Counting	—Unverified
Learning to Count Objects with Few Exemplar Annotations	May 20, 2019	ObjectObject Counting	—Unverified
SIMCO: SIMilarity-based object COunting	Apr 15, 2019	ClusteringObject	—Unverified
Towards Locally Consistent Object Counting with Constrained Multi-stage Convolutional Neural Networks	Apr 6, 2019	ObjectObject Counting	—Unverified
Object Counting and Instance Segmentation with Image-level Supervision	Mar 6, 2019	Image-level Supervised Instance SegmentationInstance Segmentation	CodeCode Available
Class-Agnostic Counting	Nov 1, 2018	Crowd CountingFew-Shot Learning	CodeCode Available
TallyQA: Answering Complex Counting Questions	Oct 29, 2018	AttributeObject Counting	CodeCode Available
Counting the uncountable: deep semantic density estimation from Space	Sep 19, 2018	Density EstimationObject	—Unverified
Dynamic Conditional Networks for Few-Shot Learning	Sep 1, 2018	Face GenerationFew-Shot Learning	—Unverified
Where are the Blobs: Counting by Localization with Point Supervision	Jul 25, 2018	ObjectObject Counting	CodeCode Available
A Statistical Method for Object Counting	Jul 22, 2018	ObjectObject Counting	—Unverified
Contextual Hourglass Networks for Segmentation and Density Estimation	Jun 8, 2018	Density EstimationImage Segmentation	—Unverified
Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images	May 28, 2018	Crowd CountingObject Counting	—Unverified
Learning Short-Cut Connections for Object Counting	May 8, 2018	Density EstimationObject	—Unverified
A-CCNN: adaptive ccnn for density estimation and crowd counting	Apr 19, 2018	Crowd CountingDensity Estimation	—Unverified

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All datasets FSC147 CARPK Pascal VOC 2007 count-test COCO count-test TallyQA-Complex TallyQA-Simple HowMany-QA PASCAL VOC Omnicount-191 TRANCOS

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	FamNet	MAE(test)	22.08	—	Unverified
2	Omnicount (Open vocabulary, multi-label, without training)	MAE(test)	18.63	—	Unverified
3	RCC	MAE(test)	17.12	—	Unverified
4	Counting-DETR	MAE(test)	16.79	—	Unverified
5	CounTX (uses text descriptions instead of visual exemplars)	MAE(test)	15.88	—	Unverified
6	LaoNet	MAE(test)	15.78	—	Unverified
7	BMNet+	MAE(test)	14.62	—	Unverified
8	SAFECount	MAE(test)	14.32	—	Unverified
9	GCA-SUN	MAE(test)	14	—	Unverified
10	SPDCN	MAE(test)	13.51	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	YOLO (2016)	MAE	156	—	Unverified
2	YOLO9000opt (2017)	MAE	130.4	—	Unverified
3	Faster R-CNN (2015)	MAE	39.88	—	Unverified
4	RetinaNet (2018)	MAE	24.58	—	Unverified
5	LPN Counting (2017)	MAE	22.76	—	Unverified
6	One-Look Regression (2016)	MAE	21.88	—	Unverified
7	RetinaNet (2018)	MAE	16.62	—	Unverified
8	CounTX (uses arbitrary text input to specify object to count, used "the cars" for CARPK)	MAE	8.13	—	Unverified
9	Soft-IoU + EM-Merger unit	MAE	6.77	—	Unverified
10	VLCounter	MAE	6.46	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Fast-RCNN	m-reIRMSE-nz	0.85	—	Unverified
2	glance-noft-2L	m-reIRMSE-nz	0.73	—	Unverified
3	LC-PSPNet	m-reIRMSE-nz	0.7	—	Unverified
4	Seq-sub-ft-3x3	m-reIRMSE-nz	0.68	—	Unverified
5	ens	m-reIRMSE-nz	0.65	—	Unverified
6	Supervised Density Map	m-reIRMSE-nz	0.61	—	Unverified
7	LC-ResFCN	m-reIRMSE-nz	0.61	—	Unverified
8	Omnicount	mRMSE	0	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Aso-sub-ft-3x3	m-reIRMSE	0.24	—	Unverified
2	glance-ft-2L	m-reIRMSE	0.23	—	Unverified
3	Fast-RCNN	m-reIRMSE	0.2	—	Unverified
4	LC-ResFCN	m-reIRMSE	0.19	—	Unverified
5	Seq-sub-ft-3x3	m-reIRMSE	0.18	—	Unverified
6	Supervised Density Map	m-reIRMSE	0.18	—	Unverified
7	ens	m-reIRMSE	0.18	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SMoLA-PaLI-X Specialist	Accuracy	77.1	—	Unverified
2	PaLI-X-VPD	Accuracy	76.6	—	Unverified
3	SMoLA-PaLI-X Generalist (0 shot)	Accuracy	70.7	—	Unverified
4	MoVie-ResNeXt	Accuracy	56.8	—	Unverified
5	RCN	Accuracy	56.2	—	Unverified
6	MoVie	Accuracy	54.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SMoLA-PaLI-X Specialist	Accuracy	86.3	—	Unverified
2	PaLI-X-VPD	Accuracy	86.2	—	Unverified
3	SMoLA-PaLI-X Generalist (0 shot)	Accuracy	83.3	—	Unverified
4	MoVie-ResNeXt	Accuracy	74.9	—	Unverified
5	RCN	Accuracy	71.8	—	Unverified
6	MoVie	Accuracy	70.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MoVie-ResNeXt	Accuracy	64	—	Unverified
2	MoVie	Accuracy	61.2	—	Unverified
3	RCN	Accuracy	60.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CEOES	mRMSE	0.42	—	Unverified
2	ILC	mRMSE	0.29	—	Unverified
3	TFOC	mRMSE	0.01	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Omnicount	mRMSE	0	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GauNet (ResNet-50)	MAE	2.1	—	Unverified