mlsanityData to 7 Oct 2026

Few-Shot Learning with Localization in Realistic Settings

arXiv 1904.08502 ↗First posted 9 Apr 2019cs.CV cs.AI cs.LGcited by 34

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Abstract (arXiv)

Traditional recognition methods typically require large, artificially-balanced training classes, while few-shot learning methods are tested on artificially small ones. In contrast to both extremes, real world recognition problems exhibit heavy-tailed class distributions, with cluttered scenes and a mix of coarse and fine-grained class distinctions. We show that prior methods designed for few-shot learning do not work out of the box in these challenging conditions, based on a new "meta-iNat" benchmark. We introduce three parameter-free improvements: (a) better training procedures based on adapting cross-validation to meta-learning, (b) novel architectures that localize objects using limited bounding box annotations before classification, and (c) simple parameter-free expansions of the feature space based on bilinear pooling. Together, these improvements double the accuracy of state-of-the-art models on meta-iNat while generalizing to prior benchmarks, complex neural architectures, and settings with substantial domain shift.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Apr 2019.

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Citing papers per year: 2021: 11; 2022: 9; 2023: 5; 2024: 2; 2025: 4; 2026: 3.

34 papers.

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  1. 2021BABEL: Bodies, Action and Behavior with English Labelscited by 261
  2. 2022Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classificationcited by 50
  3. 2021Danish Fungi 2020 -- Not Just Another Image Recognition Datasetcited by 27
  4. 2022Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learningcited by 27
  5. 2022Matching Feature Sets for Few-Shot Image Classificationcited by 25
  6. 2022Task Discrepancy Maximization for Fine-grained Few-Shot Classificationcited by 21
  7. 2023Context-Aware Meta-Learningcited by 20
  8. 2021Few-Shot Learning with Class Imbalancecited by 18
  9. 2021Learning to Affiliate: Mutual Centralized Learning for Few-shot Classificationcited by 18
  10. 2022POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samplescited by 15
  11. 2021Decomposing Convolutional Neural Networks into Reusable and Replaceable Modulescited by 14
  12. 2023CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Modelscited by 11
22 more
  1. 2021DMN4: Few-shot Learning via Discriminative Mutual Nearest Neighbor Neural Networkcited by 9
  2. 2022Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inferencecited by 9
  3. 2021Local Propagation for Few-Shot Learningcited by 4
  4. 2023Task-Oriented Channel Attention for Fine-Grained Few-Shot Classificationcited by 4
  5. 2021Streaming Self-Training via Domain-Agnostic Unlabeled Imagescited by 3
  6. 2024Unsupervised Meta-Learning via In-Context Learningcited by 3
  7. 2022Few-shot Classification with Hypersphere Modeling of Prototypescited by 2
  8. 2023An Adaptive Plug-and-Play Network for Few-Shot Learningcited by 2
  9. 2023FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?cited by 2
  10. 2026Adaptive receptive field-based spatial-frequency feature reconstruction network for fine-grained few-shot image classificationcited by 2
  11. 2021Compositional Fine-Grained Low-Shot Learningcited by 1
  12. 2021Mutual-Information Based Few-Shot Classificationcited by 1
  13. 2024Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classificationcited by 1
  14. 2021Prototypical Region Proposal Networks for Few-Shot Localization and Classificationcited by 0
  15. 2022Diagnosing and Remedying Shot Sensitivity with Cosine Few-Shot Learnerscited by 0
  16. 2022Not All Instances Contribute Equally: Instance-adaptive Class Representation Learning for Few-Shot Visual Recognitioncited by 0
  17. 2025Meta-Learning Transformers to Improve In-Context Generalizationcited by 0
  18. 2025MetaLab: Few-Shot Game Changer for Image Recognitioncited by 0
  19. 2025Color as the Impetus: Transforming Few-Shot Learnercited by 0
  20. 2025SAFA-SNN: Sparsity-Aware On-Device Few-Shot Class-Incremental Learning with Fast-Adaptive Structure of Spiking Neural Networkcited by 0
  21. 2026Universal Algorithm-Implicit Learningcited by 0
  22. 2026Phase-Aware Spatial-Frequency Fusion for Few-Shot Fine-Grained Image Classificationcited by 0