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Papers Benchmarks Datasets Tasks

CUB dataset and benchmarks – Page 4 · SOTA2 Research

Research Datasets

CUB

Benchmarks

Task NameDataset NameSOTA MethodSort

Task NameDataset NameSOTA ResultTrendResults

Generalized Category Discovery

CUB-200

79.4Accuracy (All)

25

Image Generation

CUB200 (train)

6.09Inception Score

25

Deep Metric Learning

CUB-200-2011

73.23Recall@1

24

Explanation Sparsity

CUB

0.93Gini Index

24

Attribution Faithfulness

CUB

0.144Faithfulness Correlation

24

Feature Attribution

CUB

0.65ROAD_AOPC

24

Fine-Grained Visual Recognition

CUB200 (val)

71.97Top-1 Accuracy

24

Image Classification

CUB

85.22Accuracy

24

Image Classification

CUB

82.17Accuracy

24

Generalized Zero-Shot Learning Concept Prediction

CUB

0.36MSE_U

24

Image Classification

CUB-Painting (OOD)

61.4Accuracy

24

Image Classification

CUB Painting (ID)

82.34Accuracy

24

Cause discovery for incorrect predictions

CUB-200-2011

0.262Avg Highest Confidence (0-25%)

24

Generalized Zero-Shot Learning

CUB (test)

67.8Accuracy

24

Continual Generalized Zero-Shot Learning

CUB

68.18Mean Accuracy (mSA)

24

Generalized few-shot learning

CUB

80.9Accuracy

24

5-way Few-shot Image Classification

CUB-200 2011 (meta-test)

50.621-shot Acc

24

Few-shot recognition

CUB-200-2011 (test)

68.4Top-1 Acc

24

Multi-view Classification

CUB

95.83Accuracy

23

Continual Learning

CUB-200 2011

2.76Avg Training Time per Task

21

Few-Shot Class-Incremental Learning

CUB-200

83.4Session 0 Accuracy

21

5-way classification

CUB

99.32Accuracy

21

Concept Erasure

CUB (test)

100Aer

21

Object Localization

CUB-200 (test)

74.97Top-1 Loc Acc

21

Weakly supervised object localization

CUB200-2011

56.2Loc Acc

21

Page 4 of 20

Popular tasks

Image Classification Class-incremental learning Few-shot classification Generalized Category Discovery Few-Shot Class-Incremental Learning

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