# Meta-Learning for Multi-Label Few-Shot Classification

arXiv: https://arxiv.org/abs/2110.13494 · HTML: https://mlsanity.com/paper/2110.13494/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 58 references that are in the map.
- First posted on arXiv 26 Oct 2021.

## Builds on (most cited first, 30 of 31)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning](https://mlsanity.com/paper/1903.02351/) (2019)
- [Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning](https://mlsanity.com/paper/1805.10002/) (2018)
- [CNN-RNN: A Unified Framework for Multi-label Image Classification](https://mlsanity.com/paper/1604.04573/) (2016)
- [Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification](https://mlsanity.com/paper/1702.05891/) (2017)
- [Multi-label Image Recognition by Recurrently Discovering Attentional Regions](https://mlsanity.com/paper/1711.02816/) (2017)
- [Few-Shot Learning via Saliency-guided Hallucination of Samples](https://mlsanity.com/paper/1904.03472/) (2019)
- [Improving Pairwise Ranking for Multi-label Image Classification](https://mlsanity.com/paper/1704.03135/) (2017)
- [Exploit Bounding Box Annotations for Multi-label Object Recognition](https://mlsanity.com/paper/1504.05843/) (2015)
- [LaSO: Label-Set Operations networks for multi-label few-shot learning](https://mlsanity.com/paper/1902.09811/) (2019)
- [The iMaterialist Fashion Attribute Dataset](https://mlsanity.com/paper/1906.05750/) (2019)
- [Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification](https://mlsanity.com/paper/1908.01313/) (2019)
- [Few-shot Language Coordination by Modeling Theory of Mind](https://mlsanity.com/paper/2107.05697/) (2021)
- [Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning](https://mlsanity.com/paper/2001.03919/) (2020)
- [Power Normalizations in Fine-grained Image, Few-shot Image and Graph Classification](https://mlsanity.com/paper/2012.13975/) (2020)
- [Domain Adaptation by Mixture of Alignments of Second- or Higher-Order Scatter Tensors](https://mlsanity.com/paper/1611.08195/) (2016)
- [Reinforced Attention for Few-Shot Learning and Beyond](https://mlsanity.com/paper/2104.04192/) (2021)
- [Learning an Invariant Hilbert Space for Domain Adaptation](https://mlsanity.com/paper/1611.08350/) (2016)
- [CNN-based Action Recognition and Supervised Domain Adaptation on 3D Body Skeletons via Kernel Feature Maps](https://mlsanity.com/paper/1806.09078/) (2018)
- [Power Normalizing Second-order Similarity Network for Few-shot Learning](https://mlsanity.com/paper/1811.04167/) (2018)
- [TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization with Few Labeled Samples](https://mlsanity.com/paper/2005.13820/) (2020)
- [Zero-Shot Kernel Learning](https://mlsanity.com/paper/1802.01279/) (2018)
- [Identity-preserving Face Recovery from Stylized Portraits](https://mlsanity.com/paper/1904.04241/) (2019)
- [REFINE: Random RangE FInder for Network Embedding](https://mlsanity.com/paper/2108.10703/) (2021)
- [Recovering Faces from Portraits with Auxiliary Facial Attributes](https://mlsanity.com/paper/1904.03612/) (2019)
- [Model Selection for Generalized Zero-shot Learning](https://mlsanity.com/paper/1811.03252/) (2018)

## Built on by (most cited first, 21 of 21)

- [Texts as Images in Prompt Tuning for Multi-Label Image Recognition](https://mlsanity.com/paper/2211.12739/) (2022)
- [Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinement](https://mlsanity.com/paper/2304.11598/) (2023)
- [Few-shot Keypoint Detection with Uncertainty Learning for Unseen Species](https://mlsanity.com/paper/2112.06183/) (2021)
- [Learning to Learn from APIs: Black-Box Data-Free Meta-Learning](https://mlsanity.com/paper/2305.18413/) (2023)
- [TAI++: Text as Image for Multi-Label Image Classification by Co-Learning Transferable Prompt](https://mlsanity.com/paper/2405.06926/) (2024)
- [Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning](https://mlsanity.com/paper/2303.11183/) (2023)
- [Data-free Multi-label Image Recognition via LLM-powered Prompt Tuning](https://mlsanity.com/paper/2403.01209/) (2024)
- [LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging](https://mlsanity.com/paper/2409.11264/) (2024)
- [Task-Distributionally Robust Data-Free Meta-Learning](https://mlsanity.com/paper/2311.14756/) (2023)
- [Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction](https://mlsanity.com/paper/2308.08413/) (2023)
- [Text as Image: Learning Transferable Adapter for Multi-Label Classification](https://mlsanity.com/paper/2312.04160/) (2023)
- [Text as Any-Modality for Zero-Shot Classification by Consistent Prompt Tuning](https://mlsanity.com/paper/2508.06382/) (2025)
- [Exploiting Field Dependencies for Learning on Categorical Data](https://mlsanity.com/paper/2307.09321/) (2023)
- [Dual-View Alignment Learning with Hierarchical-Prompt for Class-Imbalance Multi-Label Classification](https://mlsanity.com/paper/2509.17747/) (2025)
- [Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays](https://mlsanity.com/paper/2309.04462/) (2023)
- [Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object Structure via HyperNetworks](https://mlsanity.com/paper/2312.16218/) (2023)
- [Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning](https://mlsanity.com/paper/2506.10575/) (2025)
- [Efficient Masked Attention Transformer for Few-Shot Classification and Segmentation](https://mlsanity.com/paper/2507.23642/) (2025)
- [RAPT: Retrieval-Augmented Post-hoc Thresholding for Multi-Label Classification](https://mlsanity.com/paper/2605.16535/) (2026)
- [Adapting Dense Vision-Language Relationships for Multi-label Classification with Partial Label](https://mlsanity.com/paper/2608.22313/) (2026)
- [Purification and Regulation: Comorbidity-Aware Multi-Label Few-Shot Learning for Medical Image Classification](https://mlsanity.com/paper/2609.21541/) (2026)

## Abstract (arXiv)

Even with the luxury of having abundant data, multi-label classification is widely known to be a challenging task to address. This work targets the problem of multi-label meta-learning, where a model learns to predict multiple labels within a query (e.g., an image) by just observing a few supporting examples. In doing so, we first propose a benchmark for Few-Shot Learning (FSL) with multiple labels per sample. Next, we discuss and extend several solutions specifically designed to address the conventional and single-label FSL, to work in the multi-label regime. Lastly, we introduce a neural module to estimate the label count of a given sample by exploiting the relational inference. We will show empirically the benefit of the label count module, the label propagation algorithm, and the extensions of conventional FSL methods on three challenging datasets, namely MS-COCO, iMaterialist, and Open MIC. Overall, our thorough experiments suggest that the proposed label-propagation algorithm in conjunction with the neural label count module (NLC) shall be considered as the method of choice.