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 51–60 of 158 papers

Title	Date	Tasks	Status	Hype
Class-agnostic-Few-shot-Object-Counting	Jun 14, 2021	ObjectObject Counting	CodeCode Available	1
Learning To Count Everything	Apr 16, 2021	Exemplar-Free CountingObject Counting	CodeCode Available	1
Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-Case	Feb 24, 2021	DenoisingFew-Shot Learning	CodeCode Available	1
Heatmap-based Object Detection and Tracking with a Fully Convolutional Neural Network	Jan 10, 2021	Autonomous DrivingObject Counting	CodeCode Available	1
PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote Sensing Images	Dec 7, 2020	Crowd CountingObject	CodeCode Available	1
Synbols: Probing Learning Algorithms with Synthetic Datasets	Sep 14, 2020	Active LearningFew-Shot Learning	CodeCode Available	1
Unsupervised Domain Adaptation For Plant Organ Counting	Sep 2, 2020	Domain AdaptationObject	CodeCode Available	1
Counting from Sky: A Large-scale Dataset for Remote Sensing Object Counting and A Benchmark Method	Aug 28, 2020	Crowd CountingObject	CodeCode Available	1
Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting	Mar 12, 2020	Crowd CountingDecoder	CodeCode Available	1
Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning	Mar 5, 2020	2D Object DetectionManagement	CodeCode Available	1

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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