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Few-Shot Image Classification 벤치마크

Few-Shot Image Classification on CUB 200 5-way 5-shot

64개 결과 · ⬇ CSV · JSON

Accuracy

65.32 73.66 82.01 90.36 98.7 2017-11 2026-09 Relation Net — 65.32 (2017-11-16) Relation Net — 65.32 (2017-11-16) feat (ProtoNet) — 83.03 (2018-12-10) feat (ProtoNet) — 83.03 (2018-12-10) DN4-DA (k=1) — 81.9 (2019-03-28) DN4-DA (k=1) — 81.9 (2019-03-28) Hyperbolic ProtoNet — 72.22 (2019-04-03) Hyperbolic ProtoNet — 72.22 (2019-04-03) Self-Critique and Adapt + High-End MAML++ — 85.63 (2019-05-24) High-End MAML++ — 83.8 (2019-05-24) Self-Critique and Adapt + High-End MAML++ — 85.63 (2019-05-24) High-End MAML++ — 83.8 (2019-05-24) S2M2R — 90.85 (2019-07-28) S2M2R — 90.85 (2019-07-28) DKT + BNCosSim — 85.64 (2019-10-11) DKT + BNCosSim — 85.64 (2019-10-11) AmdimNet — 89.18 (2019-11-14) AmdimNet — 89.18 (2019-11-14) Transfer+SGC — 92.14 (2020-01-27) Transfer+SGC — 92.14 (2020-01-27) ICI — 92.48 (2020-03-26) Neg-Margin — 89.4 (2020-03-26) ICI — 92.48 (2020-03-26) Neg-Margin — 89.4 (2020-03-26) PT+MAP — 93.99 (2020-06-06) PT+MAP — 93.99 (2020-06-06) LaplacianShot — 88.68 (2020-06-29) LaplacianShot — 88.68 (2020-06-29) TIM-GD — 90.8 (2020-08-25) TIM-GD — 90.8 (2020-08-25) VFD — 91.48 (2020-10-07) VFD — 91.48 (2020-10-07) Illumination Augmentation — 96.28 (2021-02-06) Illumination Augmentation — 96.28 (2021-02-06) LST+MAP — 94.09 (2021-02-09) LST+MAP — 94.09 (2021-02-09) BD-CSPN + ESFR (ResNet-18) — 88.65 (2021-06-22) BD-CSPN + ESFR (ResNet-18) — 88.65 (2021-06-22) RENet — 91.11 (2021-08-22) RENet — 91.11 (2021-08-22) PEMnE-BMS* — 96.43 (2021-10-18) PEMnE-BMS* — 96.43 (2021-10-18) EASY 4xResNet12 (transductive) — 93.5 (2022-01-24) EASY 3xResNet12 (inductive) — 91.93 (2022-01-24) EASY 4xResNet12 (inductive) — 91.59 (2022-01-24) EASY 4xResNet12 (transductive) — 93.5 (2022-01-24) EASY 3xResNet12 (inductive) — 91.93 (2022-01-24) EASY 4xResNet12 (inductive) — 91.59 (2022-01-24) HyperShot — 80.07 (2022-03-21) HyperShot — 80.07 (2022-03-21) PT+MAP+SF+SOT (transductive) — 97.12 (2022-04-06) PT+MAP+SF+SOT (transductive) — 97.12 (2022-04-06) TDM — 93.37 (2022-07-04) TDM — 93.37 (2022-07-04) BAVARDAGE — 93.5 (2022-09-18) BAVARDAGE — 93.5 (2022-09-18) MergedNet-Max — 83.42 (2022-10-14) MergedNet-Max — 83.42 (2022-10-14) ESPT — 94.02 (2023-04-26) ESPT — 94.02 (2023-04-26) CAML [Laion-2b] — 98.7 (2023-10-17) CAML [Laion-2b] — 98.7 (2023-10-17) PT+MAP+SF+BPA (transductive) — 97.12 (2024-06-25) PT+MAP+SF+BPA (transductive) — 97.12 (2024-06-25) Relation Net — 65.32 (2017-11-16) feat (ProtoNet) — 83.03 (2018-12-10) Self-Critique and Adapt + High-End MAML++ — 85.63 (2019-05-24) S2M2R — 90.85 (2019-07-28) Transfer+SGC — 92.14 (2020-01-27) ICI — 92.48 (2020-03-26) PT+MAP — 93.99 (2020-06-06) Illumination Augmentation — 96.28 (2021-02-06) PEMnE-BMS* — 96.43 (2021-10-18) PT+MAP+SF+SOT (transductive) — 97.12 (2022-04-06) CAML [Laion-2b] — 98.7 (2023-10-17)
RankModel Accuracy Extra Training Data PaperCodeYear
1 CAML [Laion-2b] 98.7 Context-Aware Meta-Learning cfifty/CAML 2023
2 PT+MAP+SF+SOT (transductive) 97.12 The Self-Optimal-Transport Feature Transform danielshalam/bpa 2022
2 PT+MAP+SF+BPA (transductive) 97.12 The Balanced-Pairwise-Affinities Feature Transform danielshalam/bpa 2024
4 PEMnE-BMS* 96.43 Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning yhu01/bms 2021
5 Illumination Augmentation 96.28 Sill-Net: Feature Augmentation with Separated Illumination Representation lanfenghuanyu/Sill-Net 2021
6 LST+MAP 94.09 Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network ctom2/latent-space-transform 2021
7 ESPT 94.02 ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot Learning whut-yirong/espt 2023
8 PT+MAP 93.99 Leveraging the Feature Distribution in Transfer-based Few-Shot Learning sicara/easy-few-shot-learning · yhu01/PT-MAP · yhu01/bms · +3 2020
9 EASY 4xResNet12 (transductive) 93.5 EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients ybendou/easy · brain-bzh/pefsl · ybendou/fs-generalization 2022
9 BAVARDAGE 93.50 Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification 2022
11 TDM 93.37 Task Discrepancy Maximization for Fine-grained Few-Shot Classification leesb7426/cvpr2022-task-discrepancy-maximization-for-fine-grained-few-shot-classification 2022
12 ICI 92.48 Instance Credibility Inference for Few-Shot Learning Yikai-Wang/ICI-FSL 2020
13 Transfer+SGC 92.14 Graph-based Interpolation of Feature Vectors for Accurate Few-Shot Classification yhu01/transfer-sgc 2020
14 EASY 3xResNet12 (inductive) 91.93 EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients ybendou/easy · brain-bzh/pefsl · ybendou/fs-generalization 2022
15 EASY 4xResNet12 (inductive) 91.59 EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients ybendou/easy · brain-bzh/pefsl · ybendou/fs-generalization 2022
16 VFD 91.48 Variational Feature Disentangling for Fine-Grained Few-Shot Classification 2020
17 RENet 91.11 Relational Embedding for Few-Shot Classification dahyun-kang/renet 2021
18 S2M2R 90.85 Charting the Right Manifold: Manifold Mixup for Few-shot Learning ShuoYang-1998/Few_Shot_Distribution_Calibration · ShuoYang-1998/ICLR2021-Oral_Distribution_Calibration · yhu01/PT-MAP · +5 2019
19 TIM-GD 90.8 Transductive Information Maximization For Few-Shot Learning sicara/easy-few-shot-learning · mboudiaf/TIM 2020
20 Neg-Margin 89.40 Negative Margin Matters: Understanding Margin in Few-shot Classification bl0/negative-margin.few-shot 2020
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