mlsanityData to 7 Oct 2026

Transductive Few-Shot Classification on the Oblique Manifold

arXiv 2108.04009 ↗First posted 9 Aug 2021cs.CVcited by 23

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Abstract (arXiv)

Few-shot learning (FSL) attempts to learn with limited data. In this work, we perform the feature extraction in the Euclidean space and the geodesic distance metric on the Oblique Manifold (OM). Specially, for better feature extraction, we propose a non-parametric Region Self-attention with Spatial Pyramid Pooling (RSSPP), which realizes a trade-off between the generalization and the discriminative ability of the single image feature. Then, we embed the feature to OM as a point. Furthermore, we design an Oblique Distance-based Classifier (ODC) that achieves classification in the tangent spaces which better approximate OM locally by learnable tangency points. Finally, we introduce a new method for parameters initialization and a novel loss function in the transductive settings. Extensive experiments demonstrate the effectiveness of our algorithm and it outperforms state-of-the-art methods on the popular benchmarks: mini-ImageNet, tiered-ImageNet, and Caltech-UCSD Birds-200-2011 (CUB).

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 43 of its 67 references that are in the map. First posted on arXiv 9 Aug 2021.

Builds on

43 of 67 references.

References · 43

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  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  4. 2017Prototypical Networks for Few-shot Learningcited by 3,001
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31 more
  1. 2018Meta-Learning with Latent Embedding Optimizationcited by 476
  2. 2016Geometric deep learning on graphs and manifolds using mixture model CNNscited by 468
  3. 2019Meta-Learning with Differentiable Convex Optimizationcited by 461
  4. 2018Meta-Learning for Semi-Supervised Few-Shot Classificationcited by 456
  5. 2020Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?cited by 435
  6. 2020Exploring Self-attention for Image Recognitioncited by 313
  7. 2019A Baseline for Few-Shot Image Classificationcited by 298
  8. 2018Few-Shot Learning via Embedding Adaptation with Set-to-Set Functionscited by 293
  9. 2016SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioningcited by 219
  10. 2019Local Relation Networks for Image Recognitioncited by 210
  11. 2018Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learningcited by 208
  12. 2019SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learningcited by 194
  13. 2018Riemannian Adaptive Optimization Methodscited by 187
  14. 2016A Riemannian Network for SPD Matrix Learningcited by 162
  15. 2019Boosting Few-Shot Visual Learning with Self-Supervisioncited by 140
  16. 2019Charting the Right Manifold: Manifold Mixup for Few-shot Learningcited by 135
  17. 2020Interventional Few-Shot Learningcited by 112
  18. 2019Prototype Rectification for Few-Shot Learningcited by 96
  19. 2020Laplacian Regularized Few-Shot Learningcited by 90
  20. 2019Learning to Self-Train for Semi-Supervised Few-Shot Classificationcited by 83
  21. 2020DPGN: Distribution Propagation Graph Network for Few-shot Learningcited by 79
  22. 2019Meta-Curvaturecited by 70
  23. 2019Diversity with Cooperation: Ensemble Methods for Few-Shot Classificationcited by 70
  24. 2019Transductive Episodic-Wise Adaptive Metric for Few-Shot Learningcited by 67
  25. 2019Dense Classification and Implanting for Few-Shot Learningcited by 56
  26. 2019Few-Shot Learning via Saliency-guided Hallucination of Samplescited by 49
  27. 2020Empirical Bayes Transductive Meta-Learning with Synthetic Gradientscited by 48
  28. 2019A neural network based on SPD manifold learning for skeleton-based hand gesture recognitioncited by 46
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  31. 2020Few-shot Visual Reasoning with Meta-analogical Contrastive Learningcited by 19

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 9; 2024: 6; 2025: 2; 2026: 1.

23 papers.

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  1. 2022Rethinking Generalization in Few-Shot Classificationcited by 36
  2. 2023Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinementcited by 30
  3. 2023Supervised Masked Knowledge Distillation for Few-Shot Transformerscited by 23
  4. 2023A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifoldcited by 21
  5. 2022EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredientscited by 15
  6. 2024Visual Prompting for Generalized Few-shot Segmentation: A Multi-scale Approachcited by 15
  7. 2023Exploring Data Geometry for Continual Learningcited by 14
  8. 2023Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddingscited by 11
  9. 2022Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inferencecited by 9
  10. 2023A Survey on Open-Set Image Recognitioncited by 7
  11. 2023Adaptive manifold for imbalanced transductive few-shot learningcited by 4
  12. 2022Rieoptax: Riemannian Optimization in JAXcited by 1
11 more
  1. 2023Trajectory-aware Principal Manifold Framework for Data Augmentation and Image Generationcited by 1
  2. 2024Intra-task Mutual Attention based Vision Transformer for Few-Shot Learningcited by 1
  3. 2024Class-relevant Patch Embedding Selection for Few-Shot Image Classificationcited by 1
  4. 2024Harnessing the Power of Local Representations for Few-Shot Classificationcited by 1
  5. 2025Transductive One-Shot Learning Meet Subspace Decompositioncited by 1
  6. 2025Slot Attention-based Feature Filtering for Few-Shot Learningcited by 1
  7. 2022Design of the topology for contrastive visual-textual alignmentcited by 0
  8. 2023Language-Guided Reinforcement Learning for Hard Attention in Few-Shot Learningcited by 0
  9. 2024Siamese Transformer Networks for Few-shot Image Classificationcited by 0
  10. 2024A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra classcited by 0
  11. 2026Curvature-Aware Captioning:Leveraging Geodesic Attention for 3D Scene Understandingcited by 0