mlsanityData to 14 Oct 2026

Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification

arXiv 2302.08909 ↗First posted 16 Feb 2023cs.CVcited by 30

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Abstract (arXiv)

We introduce Meta-Album, an image classification meta-dataset designed to facilitate few-shot learning, transfer learning, meta-learning, among other tasks. It includes 40 open datasets, each having at least 20 classes with 40 examples per class, with verified licences. They stem from diverse domains, such as ecology (fauna and flora), manufacturing (textures, vehicles), human actions, and optical character recognition, featuring various image scales (microscopic, human scales, remote sensing). All datasets are preprocessed, annotated, and formatted uniformly, and come in 3 versions (Micro subset Mini subset Extended) to match users' computational resources. We showcase the utility of the first 30 datasets on few-shot learning problems. The other 10 will be released shortly after. Meta-Album is already more diverse and larger (in number of datasets) than similar efforts, and we are committed to keep enlarging it via a series of competitions. As competitions terminate, their test data are released, thus creating a rolling benchmark, available through OpenML.org. Our website https://meta-album.github.io/ contains the source code of challenge winning methods, baseline methods, data loaders, and instructions for contributing either new datasets or algorithms to our expandable meta-dataset.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). Builds on 41 of its 97 references that are in the map. First posted on arXiv 16 Feb 2023.

Builds on

41 of 97 references.

References · 41

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  3. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  4. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  5. 2017Prototypical Networks for Few-shot Learningcited by 3,015
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  7. 2013Describing Textures in the Wildcited by 2,125
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29 more
  1. 2019A Closer Look at Few-shot Classificationcited by 684
  2. 2017Learning multiple visual domains with residual adapterscited by 557
  3. 2017Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Uglycited by 538
  4. 2014OpenML: networked science in machine learningcited by 526
  5. 2019Meta-Learning with Implicit Gradientscited by 525
  6. 2018Meta-Learning for Semi-Supervised Few-Shot Classificationcited by 456
  7. 2020Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?cited by 437
  8. 2019A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmarkcited by 425
  9. 2018TADAM: Task dependent adaptive metric for improved few-shot learningcited by 408
  10. 2018Meta-learning with differentiable closed-form solverscited by 397
  11. 2019Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examplescited by 315
  12. 2018Visual Interpretability for Deep Learning: a Surveycited by 155
  13. 2015An open access repository of images on plant health to enable the development of mobile disease diagnosticscited by 135
  14. 2020A Survey of Deep Meta-Learningcited by 100
  15. 2020Efficient Continual Learning with Modular Networks and Task-Driven Priorscited by 88
  16. 2021Cross-domain Few-shot Learning with Task-specific Adapterscited by 80
  17. 2019PlantDoc: A Dataset for Visual Plant Disease Detectioncited by 68
  18. 2020A Universal Representation Transformer Layer for Few-Shot Image Classificationcited by 65
  19. 2021Learning a Universal Template for Few-shot Dataset Generalizationcited by 61
  20. 2021Universal Representation Learning from Multiple Domains for Few-shot Classificationcited by 56
  21. 2021Danish Fungi 2020 -- Not Just Another Image Recognition Datasetcited by 27
  22. 2021Ranking Distance Calibration for Cross-Domain Few-Shot Learningcited by 27
  23. 2021Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmarkcited by 16
  24. 2022Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019cited by 16
  25. 2020Selecting Relevant Features from a Multi-domain Representation for Few-shot Classificationcited by 11
  26. 2022Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classificationcited by 10
  27. 2021Stateless Neural Meta-Learning using Second-Order Gradientscited by 7
  28. 2022OmniPrint: A Configurable Printed Character Synthesizercited by 5
  29. 2022NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline resultscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 9; 2024: 9; 2025: 8; 2026: 4.

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  1. 2023BioCLIP: A Vision Foundation Model for the Tree of Lifecited by 161
  2. 2023Advances and Challenges in Meta-Learning: A Technical Reviewcited by 65
  3. 2024SVFT: Parameter-Efficient Fine-Tuning with Singular Vectorscited by 58
  4. 2025BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learningcited by 50
  5. 2024Diffusion-Based Neural Network Weights Generationcited by 39
  6. 2023Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and Howcited by 18
  7. 2024A comprehensive and easy-to-use multi-domain multi-task medical imaging meta-datasetcited by 12
  8. 2024Automatic Combination of Sample Selection Strategies for Few-Shot Learningcited by 5
  9. 2023Meta Omnium: A Benchmark for General-Purpose Learning-to-Learncited by 4
  10. 2023Neural Fine-Tuning Search for Few-Shot Learningcited by 4
  11. 2023Modularity in Deep Learning: A Surveycited by 3
  12. 2025Instruction-Guided Autoregressive Neural Network Parameter Generationcited by 3
18 more
  1. 2025BioCAP: Exploiting Synthetic Captions Beyond Labels in Biological Foundation Modelscited by 3
  2. 2025MetaLLMix : An XAI Aided LLM-Meta-learning Based Approach for Hyper-parameters Optimizationcited by 2
  3. 2025DALIP: Distribution Alignment-based Language-Image Pre-Training for Domain-Specific Datacited by 1
  4. 2026Universal Algorithm-Implicit Learningcited by 1
  5. 2026Energy-Efficient Plant Monitoring via Knowledge Distillationcited by 1
  6. 2023Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?cited by 0
  7. 2023dacl1k: Real-World Bridge Damage Dataset Putting Open-Source Data to the Testcited by 0
  8. 2023RRR-Net: Reusing, Reducing, and Recycling a Deep Backbone Networkcited by 0
  9. 2024Sample Weight Estimation Using Meta-Updates for Online Continual Learningcited by 0
  10. 2024Hyperparameter Tuning MLPs for Probabilistic Time Series Forecastingcited by 0
  11. 2024AI Competitions and Benchmarks: Dataset Developmentcited by 0
  12. 2024On Supernet Transfer Learning for Effective Task Adaptationcited by 0
  13. 2024FissionVAE: Federated Non-IID Image Generation with Latent Space and Decoder Decompositioncited by 0
  14. 2025Meta-Learning Transformers to Improve In-Context Generalizationcited by 0
  15. 2025Efficient Masked Attention Transformer for Few-Shot Classification and Segmentationcited by 0
  16. 2025Visual Species Recognition with Large Multimodal Models as Post-Hoc Correctorscited by 0
  17. 2026Automatic Image-Level Morphological Trait Annotation for Organismal Imagescited by 0
  18. 2026RepICL: Reusable In-Context Prediction Across Heterogeneous Representation Spacescited by 0