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Dense Classification and Implanting for Few-Shot Learning

arXiv 1903.05050 ↗First posted 12 Mar 2019cs.CVcited by 56

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Abstract (arXiv)

Training deep neural networks from few examples is a highly challenging and key problem for many computer vision tasks. In this context, we are targeting knowledge transfer from a set with abundant data to other sets with few available examples. We propose two simple and effective solutions: (i) dense classification over feature maps, which for the first time studies local activations in the domain of few-shot learning, and (ii) implanting, that is, attaching new neurons to a previously trained network to learn new, task-specific features. On miniImageNet, we improve the prior state-of-the-art on few-shot classification, i.e., we achieve 62.5%, 79.8% and 83.8% on 5-way 1-shot, 5-shot and 10-shot settings respectively.

Key facts. Cited by 56 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Mar 2019.

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Citing papers per year: 2021: 26; 2022: 14; 2023: 8; 2024: 2; 2025: 6.

56 papers.

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  1. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 162
  2. 2021Relational Embedding for Few-Shot Classificationcited by 74
  3. 2021Rectifying the Shortcut Learning of Background for Few-Shot Learningcited by 51
  4. 2022Understanding AdamW through Proximal Methods and Scale-Freenesscited by 46
  5. 2021Learning Dynamic Alignment via Meta-filter for Few-shot Learningcited by 41
  6. 2022Generating Representative Samples for Few-Shot Classificationcited by 39
  7. 2021Binocular Mutual Learning for Improving Few-shot Classificationcited by 26
  8. 2022Matching Feature Sets for Few-Shot Image Classificationcited by 25
  9. 2021Transductive Few-Shot Classification on the Oblique Manifoldcited by 23
  10. 2022Self-Promoted Supervision for Few-Shot Transformercited by 22
  11. 2021Learning to Affiliate: Mutual Centralized Learning for Few-shot Classificationcited by 18
  12. 2023From Global to Local: Multi-scale Out-of-distribution Detectioncited by 18
38 more
  1. 2022Few-Shot Learning Meets Transformer: Unified Query-Support Transformers for Few-Shot Classificationcited by 16
  2. 2025Reliable Few-shot Learning under Dual Noisescited by 16
  3. 2021Adaptive Poincar'e Point to Set Distance for Few-Shot Classificationcited by 15
  4. 2021Decomposing Convolutional Neural Networks into Reusable and Replaceable Modulescited by 14
  5. 2023The Expressive Power of Tuning Only the Normalization Layerscited by 13
  6. 2023DETA: Denoised Task Adaptation for Few-Shot Learningcited by 13
  7. 2021Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learningcited by 12
  8. 2021MFNet: Multi-class Few-shot Segmentation Network with Pixel-wise Metric Learningcited by 10
  9. 2021DMN4: Few-shot Learning via Discriminative Mutual Nearest Neighbor Neural Networkcited by 9
  10. 2022FS-BAN: Born-Again Networks for Domain Generalization Few-Shot Classificationcited by 9
  11. 2021Revisiting Local Descriptor for Improved Few-Shot Classificationcited by 8
  12. 2022MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequencescited by 8
  13. 2021Few-Shot Video Object Detectioncited by 7
  14. 2021On the Importance of Firth Bias Reduction in Few-Shot Classificationcited by 7
  15. 2021Tensor feature hallucination for few-shot learningcited by 6
  16. 2021Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encodingcited by 5
  17. 2021MDFM: Multi-Decision Fusing Model for Few-Shot Learningcited by 5
  18. 2022GCT: Graph Co-Training for Semi-Supervised Few-Shot Learningcited by 5
  19. 2025Dual-level Mixup for Graph Few-shot Learning with Fewer Taskscited by 5
  20. 2021Local Propagation for Few-Shot Learningcited by 4
  21. 2022Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross Modulationcited by 4
  22. 2023Adaptive manifold for imbalanced transductive few-shot learningcited by 4
  23. 2023Embedding Space Interpolation Beyond Mini-Batch, Beyond Pairs and Beyond Examplescited by 4
  24. 2024Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention Mechanismscited by 4
  25. 2022A Task-aware Dual Similarity Network for Fine-grained Few-shot Learningcited by 3
  26. 2022Teach me how to Interpolate a Myriad of Embeddingscited by 2
  27. 2024Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property Predictioncited by 2
  28. 2021Trainable Class Prototypes for Few-Shot Learningcited by 1
  29. 2021Learning Class-level Prototypes for Few-shot Learningcited by 1
  30. 2021Domain Agnostic Few-Shot Learning For Document Intelligencecited by 1
  31. 2021Learning Online for Unified Segmentation and Tracking Modelscited by 1
  32. 2022Uncertainty-based Network for Few-shot Image Classificationcited by 1
  33. 2021Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samplescited by 0
  34. 2021Self-Denoising Neural Networks for Few Shot Learningcited by 0
  35. 2021Few-shot learning with improved local representations via bias rectify modulecited by 0
  36. 2021Dual Path Structural Contrastive Embeddings for Learning Novel Objectscited by 0
  37. 2022Learning Primitive-aware Discriminative Representations for Few-shot Learningcited by 0
  38. 2023Rethinking matching-based few-shot action recognitioncited by 0

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