felixleungsc/paperswithcode-data-evaluation-tables

task_path stringlengths 3 199 ⌀	dataset stringlengths 1 128 ⌀	model_name stringlengths 1 223 ⌀	paper_url stringlengths 21 601 ⌀	metric_name stringlengths 1 50 ⌀	metric_value stringlengths 1 9.22k ⌀
Active Speaker Detection > Fraud Detection	Yelp-Fraud	LEX-GNN	https://dl.acm.org/doi/10.1145/3627673.3679956	Averaged Precision	83.56
Active Speaker Detection > Fraud Detection	Yelp-Fraud	LEX-GNN	https://dl.acm.org/doi/10.1145/3627673.3679956	F1 Macro	86.35
Active Speaker Detection > Fraud Detection	Yelp-Fraud	LEX-GNN	https://dl.acm.org/doi/10.1145/3627673.3679956	G-mean	84.91
Active Speaker Detection > Fraud Detection	Yelp-Fraud	JA-GNN	https://arxiv.org/abs/2411.05857v2	AUC-ROC	95.11
Active Speaker Detection > Fraud Detection	Yelp-Fraud	GTAN	https://arxiv.org/abs/2412.18287v1	AUC-ROC	94.98
Active Speaker Detection > Fraud Detection	Yelp-Fraud	GTAN	https://arxiv.org/abs/2412.18287v1	Averaged Precision	82.41
Active Speaker Detection > Fraud Detection	Yelp-Fraud	BOLT-GRAPH	https://arxiv.org/abs/2303.17727v4	AUC-ROC	93.18
Active Speaker Detection > Fraud Detection	Yelp-Fraud	SplitGNN	https://dl.acm.org/doi/10.1145/3583780.3615067	AUC-ROC	92.03
Active Speaker Detection > Fraud Detection	Yelp-Fraud	GAT+JK	https://arxiv.org/abs/2104.01404v2	AUC-ROC	90.04
Active Speaker Detection > Fraud Detection	Yelp-Fraud	RLC-GNN	https://www.mdpi.com/2076-3417/11/12/5656/htm	AUC-ROC	85.44
Active Speaker Detection > Fraud Detection	Yelp-Fraud	RioGNN	https://arxiv.org/abs/2104.07886v2	AUC-ROC	83.54
Active Speaker Detection > Fraud Detection	Yelp-Fraud	PC-GNN	https://dl.acm.org/doi/abs/10.1145/3442381.3449989	AUC-ROC	79.87
Active Speaker Detection > Fraud Detection	Yelp-Fraud	PC-GNN	https://dl.acm.org/doi/abs/10.1145/3442381.3449989	Averaged Precision	48.10
Active Speaker Detection > Fraud Detection	Yelp-Fraud	CARE-GNN	https://arxiv.org/abs/2008.08692v1	AUC-ROC	75.70
Active Speaker Detection > Fraud Detection	Yelp-Fraud	CARE-GNN	https://arxiv.org/abs/2008.08692v1	Averaged Precision	42.68
Active Speaker Detection > Fraud Detection	Elliptic Dataset	GCN	https://arxiv.org/abs/2405.19383v3	AUPRC	0.5946
Active Speaker Detection > Fraud Detection	Elliptic Dataset	GCN	https://arxiv.org/abs/2405.19383v3	AUC	0.8329
Active Speaker Detection > Fraud Detection	Elliptic Dataset	GraphSAGE	https://arxiv.org/abs/2405.19383v3	AUPRC	0.6312
Active Speaker Detection > Fraud Detection	Elliptic Dataset	GraphSAGE	https://arxiv.org/abs/2405.19383v3	AUC	0.8279
Active Speaker Detection > Fraud Detection	Elliptic Dataset	GAT	https://arxiv.org/abs/2405.19383v3	AUPRC	0.5886
Active Speaker Detection > Fraud Detection	Elliptic Dataset	GAT	https://arxiv.org/abs/2405.19383v3	AUC	0.8102
Active Speaker Detection > Fraud Detection	Elliptic Dataset	GIN	https://arxiv.org/abs/2405.19383v3	AUPRC	0.5517
Active Speaker Detection > Fraud Detection	Elliptic Dataset	GIN	https://arxiv.org/abs/2405.19383v3	AUC	0.8089
Active Speaker Detection > Fraud Detection	Elliptic Dataset	Node2vec	https://arxiv.org/abs/2405.19383v3	AUPRC	0.0594
Active Speaker Detection > Fraud Detection	Elliptic Dataset	Node2vec	https://arxiv.org/abs/2405.19383v3	AUC	0.5263
Active Speaker Detection > Fraud Detection	Elliptic Dataset	Deepwalk	https://arxiv.org/abs/2405.19383v3	AUPRC	0.0488
Active Speaker Detection > Fraud Detection	Elliptic Dataset	Deepwalk	https://arxiv.org/abs/2405.19383v3	AUC	0.4500
Active Speaker Detection > Fraud Detection	BAF – Variant III	1D-CSNN	https://link.springer.com/chapter/10.1007/978-3-031-76604-6_4	Recall @ 5% FPR	41.83%
Active Speaker Detection > Fraud Detection	FDCompCN	SplitGNN	https://dl.acm.org/doi/10.1145/3583780.3615067	AUC-ROC	68.98
Active Speaker Detection > Fraud Detection	Amazon-Fraud	LEX-GNN	https://dl.acm.org/doi/10.1145/3627673.3679956	AUC-ROC	97.91
Active Speaker Detection > Fraud Detection	Amazon-Fraud	LEX-GNN	https://dl.acm.org/doi/10.1145/3627673.3679956	Averaged Precision	92.18
Active Speaker Detection > Fraud Detection	Amazon-Fraud	LEX-GNN	https://dl.acm.org/doi/10.1145/3627673.3679956	F1 Macro	93.48
Active Speaker Detection > Fraud Detection	Amazon-Fraud	LEX-GNN	https://dl.acm.org/doi/10.1145/3627673.3679956	G-mean	92.03
Active Speaker Detection > Fraud Detection	Amazon-Fraud	GTAN	https://arxiv.org/abs/2412.18287v1	AUC-ROC	97.50
Active Speaker Detection > Fraud Detection	Amazon-Fraud	GTAN	https://arxiv.org/abs/2412.18287v1	Averaged Precision	89.26
Active Speaker Detection > Fraud Detection	Amazon-Fraud	RLC-GNN	https://www.mdpi.com/2076-3417/11/12/5656/htm	AUC-ROC	97.48
Active Speaker Detection > Fraud Detection	Amazon-Fraud	RioGNN	https://arxiv.org/abs/2104.07886v2	AUC-ROC	96.19
Active Speaker Detection > Fraud Detection	Amazon-Fraud	PC-GNN	https://dl.acm.org/doi/abs/10.1145/3442381.3449989	AUC-ROC	95.86
Active Speaker Detection > Fraud Detection	Amazon-Fraud	PC-GNN	https://dl.acm.org/doi/abs/10.1145/3442381.3449989	Averaged Precision	85.49
Active Speaker Detection > Fraud Detection	Amazon-Fraud	CARE-GNN	https://arxiv.org/abs/2008.08692v1	AUC-ROC	89.73
Active Speaker Detection > Fraud Detection	Amazon-Fraud	CARE-GNN	https://arxiv.org/abs/2008.08692v1	Averaged Precision	82.19
Object Rearrangement	Open6DOR V2	SoFar	https://arxiv.org/abs/2502.13143v1	pos-level0	96.0
Object Rearrangement	Open6DOR V2	SoFar	https://arxiv.org/abs/2502.13143v1	6-DoF	48.7
Object Rearrangement	Open6DOR V2	SoFar	https://arxiv.org/abs/2502.13143v1	pos-level1	81.5
Object Rearrangement	Open6DOR V2	SoFar	https://arxiv.org/abs/2502.13143v1	rot-level0	68.6
Object Rearrangement	Open6DOR V2	SoFar	https://arxiv.org/abs/2502.13143v1	rot-level1	42.2
Object Rearrangement	Open6DOR V2	SoFar	https://arxiv.org/abs/2502.13143v1	rot-level2	70.1
Object Rearrangement	Open6DOR V2	Open6DOR	https://pku-epic.github.io/Open6DOR/	pos-level0	60.3
Object Rearrangement	Open6DOR V2	Open6DOR	https://pku-epic.github.io/Open6DOR/	6-DoF	35.6
Object Rearrangement	Open6DOR V2	Open6DOR	https://pku-epic.github.io/Open6DOR/	pos-level1	78.6
Object Rearrangement	Open6DOR V2	Open6DOR	https://pku-epic.github.io/Open6DOR/	rot-level0	45.7
Object Rearrangement	Open6DOR V2	Open6DOR	https://pku-epic.github.io/Open6DOR/	rot-level1	32.5
Object Rearrangement	Open6DOR V2	Open6DOR	https://pku-epic.github.io/Open6DOR/	rot-level2	49.8
Object Rearrangement	Open6DOR V2	Dream2Real	https://arxiv.org/abs/2312.04533v2	pos-level0	11.0
Object Rearrangement	Open6DOR V2	Dream2Real	https://arxiv.org/abs/2312.04533v2	6-DoF	13.5
Object Rearrangement	Open6DOR V2	Dream2Real	https://arxiv.org/abs/2312.04533v2	pos-level1	17.2
Object Rearrangement	Open6DOR V2	Dream2Real	https://arxiv.org/abs/2312.04533v2	rot-level0	37.3
Object Rearrangement	Open6DOR V2	Dream2Real	https://arxiv.org/abs/2312.04533v2	rot-level1	27.6
Object Rearrangement	Open6DOR V2	Dream2Real	https://arxiv.org/abs/2312.04533v2	rot-level2	26.2
Object Rearrangement	Open6DOR V2	GPT-4V	https://arxiv.org/abs/2303.08774v5	pos-level0	39.1
Object Rearrangement	Open6DOR V2	GPT-4V	https://arxiv.org/abs/2303.08774v5	6-DoF	-
Object Rearrangement	Open6DOR V2	GPT-4V	https://arxiv.org/abs/2303.08774v5	pos-level1	46.8
Object Rearrangement	Open6DOR V2	GPT-4V	https://arxiv.org/abs/2303.08774v5	rot-level0	9.1
Object Rearrangement	Open6DOR V2	GPT-4V	https://arxiv.org/abs/2303.08774v5	rot-level1	6.9
Object Rearrangement	Open6DOR V2	GPT-4V	https://arxiv.org/abs/2303.08774v5	rot-level2	11.7
Object Rearrangement	Open6DOR V2	VoxPoser	https://arxiv.org/abs/2307.05973v2	pos-level0	21.7
Object Rearrangement	Open6DOR V2	VoxPoser	https://arxiv.org/abs/2307.05973v2	6-DoF	-
Object Rearrangement	Open6DOR V2	VoxPoser	https://arxiv.org/abs/2307.05973v2	pos-level1	35.6
Object Rearrangement	Open6DOR V2	VoxPoser	https://arxiv.org/abs/2307.05973v2	rot-level0	-
Object Rearrangement	Open6DOR V2	VoxPoser	https://arxiv.org/abs/2307.05973v2	rot-level1	-
Object Rearrangement	Open6DOR V2	VoxPoser	https://arxiv.org/abs/2307.05973v2	rot-level2	-
class-incremental learning	cifar100	EWC	http://arxiv.org/abs/1612.00796v2	10-stage average accuracy	50.53
ECG Digitization	ECG-Image-Database	ECG-Digitiser	https://arxiv.org/abs/2410.14185v1	SNR	12.15
ECG Digitization	ECG-Image-Database	BAPORLab	https://cinc.org/archives/2024/pdf/CinC2024-227.pdf	SNR	5.493
ECG Digitization	ECG-Image-Database	WAVIE	https://cinc.org/archives/2024/pdf/CinC2024-229.pdf	SNR	5.469
Multi-class Classification	Reuters-52	SVM (tficf)	http://arxiv.org/abs/1012.2609v4	Macro F1	73.9
Multi-class Classification	Training and validation dataset of capsule vision 2024 challenge.	Multi-Model Ensemble	https://arxiv.org/abs/2410.18879v2	Mean AUC	0.9908
Multi-class Classification	COVID-19 CXR Dataset	COVID-CXNet	https://arxiv.org/abs/2006.13807v2	Accuracy (%)	94.2
Multi-class Classification	COVID chest X-ray	COVID-ResNet	https://arxiv.org/abs/2003.14395v1	F1 score	0.8967
Multi-class Classification	TII-SSRC-23	Extra Trees	https://arxiv.org/abs/2310.10661v1	F1-Score	93.36
Video Anomaly Detection	HR-ShanghaiTech	PoseWatch-H	https://arxiv.org/abs/2408.15185v1	AUC	87.23
Video Anomaly Detection	HR-ShanghaiTech	MoPRL	https://arxiv.org/abs/2112.03649v2	AUC	84.3
Video Anomaly Detection	HR-ShanghaiTech	TSGAD	https://arxiv.org/abs/2406.15395v1	AUC	81.77
Video Anomaly Detection	HR-ShanghaiTech	TrajREC	https://arxiv.org/abs/2311.01851v1	AUC	77.9
Video Anomaly Detection	HR-ShanghaiTech	MoCoDAD	https://arxiv.org/abs/2307.07205v3	AUC	77.6
Video Anomaly Detection	HR-ShanghaiTech	COSKAD-euclidean	https://arxiv.org/abs/2301.09489v5	AUC	77.1
Video Anomaly Detection	HR-ShanghaiTech	Multi-timescale Prediction	https://arxiv.org/abs/1908.04321v1	AUC	77.0
Video Anomaly Detection	HR-ShanghaiTech	COSKAD-hyperbolic	https://arxiv.org/abs/2301.09489v5	AUC	75.6
Video Anomaly Detection	HR-ShanghaiTech	MPED-RNN	http://arxiv.org/abs/1903.03295v2	AUC	75.4
Video Anomaly Detection	HR-ShanghaiTech	COSKAD-radial	https://arxiv.org/abs/2301.09489v5	AUC	75.2
Video Anomaly Detection	HR-ShanghaiTech	BiPOCO	https://arxiv.org/abs/2207.02281v1	AUC	74.9
Video Anomaly Detection	HR-ShanghaiTech	GEPC	https://arxiv.org/abs/1912.11850v2	AUC	74.8
Video Anomaly Detection	HR-ShanghaiTech	Pred	http://arxiv.org/abs/1712.09867v3	AUC	72.7
Video Anomaly Detection	HR-ShanghaiTech	Conv-AE	http://arxiv.org/abs/1604.04574v1	AUC	69.8
Video Anomaly Detection	Ped2	HF2-VAD	https://arxiv.org/abs/2108.06852v1	AUC	0.993
Video Anomaly Detection	IITB Corridor	VideoPatchCore	https://arxiv.org/abs/2409.16225v5	AUC	76.4%
Video Anomaly Detection	HR-Avenue	TrajREC	https://arxiv.org/abs/2311.01851v1	AUC	89.4
Video Anomaly Detection	HR-Avenue	MoCoDAD	https://arxiv.org/abs/2307.07205v3	AUC	89.0
Video Anomaly Detection	HR-Avenue	Multi-timescale Prediction	https://arxiv.org/abs/1908.04321v1	AUC	88.33
Video Anomaly Detection	HR-Avenue	COSKAD-euclidean	https://arxiv.org/abs/2301.09489v5	AUC	87.8

Active Speaker Detection > Fraud Detection

Yelp-Fraud

LEX-GNN

https://dl.acm.org/doi/10.1145/3627673.3679956

Averaged Precision

83.56

Active Speaker Detection > Fraud Detection

Yelp-Fraud

LEX-GNN

https://dl.acm.org/doi/10.1145/3627673.3679956

F1 Macro

86.35

Active Speaker Detection > Fraud Detection

Yelp-Fraud

LEX-GNN

https://dl.acm.org/doi/10.1145/3627673.3679956

G-mean

84.91

Active Speaker Detection > Fraud Detection

Yelp-Fraud

JA-GNN

https://arxiv.org/abs/2411.05857v2

AUC-ROC

95.11

Active Speaker Detection > Fraud Detection

Yelp-Fraud

GTAN

https://arxiv.org/abs/2412.18287v1

AUC-ROC

94.98

Active Speaker Detection > Fraud Detection

Yelp-Fraud

GTAN

https://arxiv.org/abs/2412.18287v1

Averaged Precision

82.41

Active Speaker Detection > Fraud Detection

Yelp-Fraud

BOLT-GRAPH

https://arxiv.org/abs/2303.17727v4

AUC-ROC

93.18

Active Speaker Detection > Fraud Detection

Yelp-Fraud

SplitGNN

https://dl.acm.org/doi/10.1145/3583780.3615067

AUC-ROC

92.03

Active Speaker Detection > Fraud Detection

Yelp-Fraud

GAT+JK

https://arxiv.org/abs/2104.01404v2

AUC-ROC

90.04

Active Speaker Detection > Fraud Detection

Yelp-Fraud

RLC-GNN

https://www.mdpi.com/2076-3417/11/12/5656/htm

AUC-ROC

85.44

Active Speaker Detection > Fraud Detection

Yelp-Fraud

RioGNN

https://arxiv.org/abs/2104.07886v2

AUC-ROC

83.54

Active Speaker Detection > Fraud Detection

Yelp-Fraud

PC-GNN

https://dl.acm.org/doi/abs/10.1145/3442381.3449989

AUC-ROC

79.87

Active Speaker Detection > Fraud Detection

Yelp-Fraud

PC-GNN

https://dl.acm.org/doi/abs/10.1145/3442381.3449989

Averaged Precision

48.10

Active Speaker Detection > Fraud Detection

Yelp-Fraud

CARE-GNN

https://arxiv.org/abs/2008.08692v1

AUC-ROC

75.70

Active Speaker Detection > Fraud Detection

Yelp-Fraud

CARE-GNN

https://arxiv.org/abs/2008.08692v1

Averaged Precision

42.68

Active Speaker Detection > Fraud Detection

Elliptic Dataset

GCN

https://arxiv.org/abs/2405.19383v3

AUPRC

0.5946

Active Speaker Detection > Fraud Detection

Elliptic Dataset

GCN

https://arxiv.org/abs/2405.19383v3

AUC

0.8329

Active Speaker Detection > Fraud Detection

Elliptic Dataset

GraphSAGE

https://arxiv.org/abs/2405.19383v3

AUPRC

0.6312

Active Speaker Detection > Fraud Detection

Elliptic Dataset

GraphSAGE

https://arxiv.org/abs/2405.19383v3

AUC

0.8279

Active Speaker Detection > Fraud Detection

Elliptic Dataset

GAT

https://arxiv.org/abs/2405.19383v3

AUPRC

0.5886

Active Speaker Detection > Fraud Detection

Elliptic Dataset

GAT

https://arxiv.org/abs/2405.19383v3

AUC

0.8102

Active Speaker Detection > Fraud Detection

Elliptic Dataset

GIN

https://arxiv.org/abs/2405.19383v3

AUPRC

0.5517

Active Speaker Detection > Fraud Detection

Elliptic Dataset

GIN

https://arxiv.org/abs/2405.19383v3

AUC

0.8089

Active Speaker Detection > Fraud Detection

Elliptic Dataset

Node2vec

https://arxiv.org/abs/2405.19383v3

AUPRC

0.0594

Active Speaker Detection > Fraud Detection

Elliptic Dataset

Node2vec

https://arxiv.org/abs/2405.19383v3

AUC

0.5263

Active Speaker Detection > Fraud Detection

Elliptic Dataset

Deepwalk

https://arxiv.org/abs/2405.19383v3

AUPRC

0.0488

Active Speaker Detection > Fraud Detection

Elliptic Dataset

Deepwalk

https://arxiv.org/abs/2405.19383v3

AUC

0.4500

Active Speaker Detection > Fraud Detection

BAF – Variant III

1D-CSNN

https://link.springer.com/chapter/10.1007/978-3-031-76604-6_4

Recall @ 5% FPR

41.83%

Active Speaker Detection > Fraud Detection

FDCompCN

SplitGNN

https://dl.acm.org/doi/10.1145/3583780.3615067

AUC-ROC

68.98

Active Speaker Detection > Fraud Detection

Amazon-Fraud

LEX-GNN

https://dl.acm.org/doi/10.1145/3627673.3679956

AUC-ROC

97.91

Active Speaker Detection > Fraud Detection

Amazon-Fraud

LEX-GNN

https://dl.acm.org/doi/10.1145/3627673.3679956

Averaged Precision

92.18

Active Speaker Detection > Fraud Detection

Amazon-Fraud

LEX-GNN

https://dl.acm.org/doi/10.1145/3627673.3679956

F1 Macro

93.48

Active Speaker Detection > Fraud Detection

Amazon-Fraud

LEX-GNN

https://dl.acm.org/doi/10.1145/3627673.3679956

G-mean

92.03

Active Speaker Detection > Fraud Detection

Amazon-Fraud

GTAN

https://arxiv.org/abs/2412.18287v1

AUC-ROC

97.50

Active Speaker Detection > Fraud Detection

Amazon-Fraud

GTAN

https://arxiv.org/abs/2412.18287v1

Averaged Precision

89.26

Active Speaker Detection > Fraud Detection

Amazon-Fraud

RLC-GNN

https://www.mdpi.com/2076-3417/11/12/5656/htm

AUC-ROC

97.48

Active Speaker Detection > Fraud Detection

Amazon-Fraud

RioGNN

https://arxiv.org/abs/2104.07886v2

AUC-ROC

96.19

Active Speaker Detection > Fraud Detection

Amazon-Fraud

PC-GNN

https://dl.acm.org/doi/abs/10.1145/3442381.3449989

AUC-ROC

95.86

Active Speaker Detection > Fraud Detection

Amazon-Fraud

PC-GNN

https://dl.acm.org/doi/abs/10.1145/3442381.3449989

Averaged Precision

85.49

Active Speaker Detection > Fraud Detection

Amazon-Fraud

CARE-GNN

https://arxiv.org/abs/2008.08692v1

AUC-ROC

89.73

Active Speaker Detection > Fraud Detection

Amazon-Fraud

CARE-GNN

https://arxiv.org/abs/2008.08692v1

Averaged Precision

82.19

Object Rearrangement

Open6DOR V2

SoFar

https://arxiv.org/abs/2502.13143v1

pos-level0

96.0

Object Rearrangement

Open6DOR V2

SoFar

https://arxiv.org/abs/2502.13143v1

6-DoF

48.7

Object Rearrangement

Open6DOR V2

SoFar

https://arxiv.org/abs/2502.13143v1

pos-level1

81.5

Object Rearrangement

Open6DOR V2

SoFar

https://arxiv.org/abs/2502.13143v1

rot-level0

68.6

Object Rearrangement

Open6DOR V2

SoFar

https://arxiv.org/abs/2502.13143v1

rot-level1

42.2

Object Rearrangement

Open6DOR V2

SoFar

https://arxiv.org/abs/2502.13143v1

rot-level2

70.1

Object Rearrangement

Open6DOR V2

Open6DOR

https://pku-epic.github.io/Open6DOR/

pos-level0

60.3

Object Rearrangement

Open6DOR V2

Open6DOR

https://pku-epic.github.io/Open6DOR/

6-DoF

35.6

Object Rearrangement

Open6DOR V2

Open6DOR

https://pku-epic.github.io/Open6DOR/

pos-level1

78.6

Object Rearrangement

Open6DOR V2

Open6DOR

https://pku-epic.github.io/Open6DOR/

rot-level0

45.7

Object Rearrangement

Open6DOR V2

Open6DOR

https://pku-epic.github.io/Open6DOR/

rot-level1

32.5

Object Rearrangement

Open6DOR V2

Open6DOR

https://pku-epic.github.io/Open6DOR/

rot-level2

49.8

Object Rearrangement

Open6DOR V2

Dream2Real

https://arxiv.org/abs/2312.04533v2

pos-level0

11.0

Object Rearrangement

Open6DOR V2

Dream2Real

https://arxiv.org/abs/2312.04533v2

6-DoF

13.5

Object Rearrangement

Open6DOR V2

Dream2Real

https://arxiv.org/abs/2312.04533v2

pos-level1

17.2

Object Rearrangement

Open6DOR V2

Dream2Real

https://arxiv.org/abs/2312.04533v2

rot-level0

37.3

Object Rearrangement

Open6DOR V2

Dream2Real

https://arxiv.org/abs/2312.04533v2

rot-level1

27.6

Object Rearrangement

Open6DOR V2

Dream2Real

https://arxiv.org/abs/2312.04533v2

rot-level2

26.2

Object Rearrangement

Open6DOR V2

GPT-4V

https://arxiv.org/abs/2303.08774v5

pos-level0

39.1

Object Rearrangement

Open6DOR V2

GPT-4V

https://arxiv.org/abs/2303.08774v5

6-DoF

-

Object Rearrangement

Open6DOR V2

GPT-4V

https://arxiv.org/abs/2303.08774v5

pos-level1

46.8

Object Rearrangement

Open6DOR V2

GPT-4V

https://arxiv.org/abs/2303.08774v5

rot-level0

9.1

Object Rearrangement

Open6DOR V2

GPT-4V

https://arxiv.org/abs/2303.08774v5

rot-level1

6.9

Object Rearrangement

Open6DOR V2

GPT-4V

https://arxiv.org/abs/2303.08774v5

rot-level2

11.7

Object Rearrangement

Open6DOR V2

VoxPoser

https://arxiv.org/abs/2307.05973v2

pos-level0

21.7

Object Rearrangement

Open6DOR V2

VoxPoser

https://arxiv.org/abs/2307.05973v2

6-DoF

-

Object Rearrangement

Open6DOR V2

VoxPoser

https://arxiv.org/abs/2307.05973v2

pos-level1

35.6

Object Rearrangement

Open6DOR V2

VoxPoser

https://arxiv.org/abs/2307.05973v2

rot-level0

-

Object Rearrangement

Open6DOR V2

VoxPoser

https://arxiv.org/abs/2307.05973v2

rot-level1

-

Object Rearrangement

Open6DOR V2

VoxPoser

https://arxiv.org/abs/2307.05973v2

rot-level2

-

class-incremental learning

cifar100

EWC

http://arxiv.org/abs/1612.00796v2

10-stage average accuracy

50.53

ECG Digitization

ECG-Image-Database

ECG-Digitiser

https://arxiv.org/abs/2410.14185v1

SNR

12.15

ECG Digitization

ECG-Image-Database

BAPORLab

https://cinc.org/archives/2024/pdf/CinC2024-227.pdf

SNR

5.493

ECG Digitization

ECG-Image-Database

WAVIE

https://cinc.org/archives/2024/pdf/CinC2024-229.pdf

SNR

5.469

Multi-class Classification

Reuters-52

SVM (tficf)

http://arxiv.org/abs/1012.2609v4

Macro F1

73.9

Multi-class Classification

Training and validation dataset of capsule vision 2024 challenge.

Multi-Model Ensemble

https://arxiv.org/abs/2410.18879v2

Mean AUC

0.9908

Multi-class Classification

COVID-19 CXR Dataset

COVID-CXNet

https://arxiv.org/abs/2006.13807v2

Accuracy (%)

94.2

Multi-class Classification

COVID chest X-ray

COVID-ResNet

https://arxiv.org/abs/2003.14395v1

F1 score

0.8967

Multi-class Classification

TII-SSRC-23

Extra Trees

https://arxiv.org/abs/2310.10661v1

F1-Score

93.36

Video Anomaly Detection

HR-ShanghaiTech

PoseWatch-H

https://arxiv.org/abs/2408.15185v1

AUC

87.23

Video Anomaly Detection

HR-ShanghaiTech

MoPRL

https://arxiv.org/abs/2112.03649v2

AUC

84.3

Video Anomaly Detection

HR-ShanghaiTech

TSGAD

https://arxiv.org/abs/2406.15395v1

AUC

81.77

Video Anomaly Detection

HR-ShanghaiTech

TrajREC

https://arxiv.org/abs/2311.01851v1

AUC

77.9

Video Anomaly Detection

HR-ShanghaiTech

MoCoDAD

https://arxiv.org/abs/2307.07205v3

AUC

77.6

Video Anomaly Detection

HR-ShanghaiTech

COSKAD-euclidean

https://arxiv.org/abs/2301.09489v5

AUC

77.1

Video Anomaly Detection

HR-ShanghaiTech

Multi-timescale Prediction

https://arxiv.org/abs/1908.04321v1

AUC

77.0

Video Anomaly Detection

HR-ShanghaiTech

COSKAD-hyperbolic

https://arxiv.org/abs/2301.09489v5

AUC

75.6

Video Anomaly Detection

HR-ShanghaiTech

MPED-RNN

http://arxiv.org/abs/1903.03295v2

AUC

75.4

Video Anomaly Detection

HR-ShanghaiTech

COSKAD-radial

https://arxiv.org/abs/2301.09489v5

AUC

75.2

Video Anomaly Detection

HR-ShanghaiTech

BiPOCO

https://arxiv.org/abs/2207.02281v1

AUC

74.9

Video Anomaly Detection

HR-ShanghaiTech

GEPC

https://arxiv.org/abs/1912.11850v2

AUC

74.8

Video Anomaly Detection

HR-ShanghaiTech

Pred

http://arxiv.org/abs/1712.09867v3

AUC

72.7

Video Anomaly Detection

HR-ShanghaiTech

Conv-AE

http://arxiv.org/abs/1604.04574v1

AUC

69.8

Video Anomaly Detection

Ped2

HF2-VAD

https://arxiv.org/abs/2108.06852v1

AUC

0.993

Video Anomaly Detection

IITB Corridor

VideoPatchCore

https://arxiv.org/abs/2409.16225v5

AUC

76.4%

Video Anomaly Detection

HR-Avenue

TrajREC

https://arxiv.org/abs/2311.01851v1

AUC

89.4

Video Anomaly Detection

HR-Avenue

MoCoDAD

https://arxiv.org/abs/2307.07205v3

AUC

89.0

Video Anomaly Detection

HR-Avenue

Multi-timescale Prediction

https://arxiv.org/abs/1908.04321v1

AUC

88.33

Video Anomaly Detection

HR-Avenue

COSKAD-euclidean

https://arxiv.org/abs/2301.09489v5

AUC

87.8