# Baby steps towards few-shot learning with multiple semantics

arXiv: https://arxiv.org/abs/1906.01905 · HTML: https://mlsanity.com/paper/1906.01905/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 29 of 29)

- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Using Shape to Categorize: Low-Shot Learning with an Explicit Shape Bias](https://mlsanity.com/paper/2101.07296/) (2021)
- [Counterfactual Zero-Shot and Open-Set Visual Recognition](https://mlsanity.com/paper/2103.00887/) (2021)
- [Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models](https://mlsanity.com/paper/2301.06267/) (2023)
- [Generating images of rare concepts using pre-trained diffusion models](https://mlsanity.com/paper/2304.14530/) (2023)
- [Subspace Regularizers for Few-Shot Class Incremental Learning](https://mlsanity.com/paper/2110.07059/) (2021)
- [Norm-guided latent space exploration for text-to-image generation](https://mlsanity.com/paper/2306.08687/) (2023)
- [Improving neural network representations using human similarity judgments](https://mlsanity.com/paper/2306.04507/) (2023)
- [FHIST: A Benchmark for Few-shot Classification of Histological Images](https://mlsanity.com/paper/2206.00092/) (2022)
- [Compositional Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2405.17022/) (2024)
- [Rich Semantics Improve Few-shot Learning](https://mlsanity.com/paper/2104.12709/) (2021)
- [SEGA: Semantic Guided Attention on Visual Prototype for Few-Shot Learning](https://mlsanity.com/paper/2111.04316/) (2021)
- [Multimodality in Meta-Learning: A Comprehensive Survey](https://mlsanity.com/paper/2109.13576/) (2021)
- [Prototype Completion for Few-Shot Learning](https://mlsanity.com/paper/2108.05010/) (2021)
- [Information Symmetry Matters: A Modal-Alternating Propagation Network for Few-Shot Learning](https://mlsanity.com/paper/2109.01295/) (2021)
- [Dizygotic Conditional Variational AutoEncoder for Multi-Modal and Partial Modality Absent Few-Shot Learning](https://mlsanity.com/paper/2106.14467/) (2021)
- [Visual-Semantic Contrastive Alignment for Few-Shot Image Classification](https://mlsanity.com/paper/2210.11000/) (2022)
- [Cross-Modal Augmentation for Few-Shot Multimodal Fake News Detection](https://mlsanity.com/paper/2407.12880/) (2024)
- [MetaDT: Meta Decision Tree with Class Hierarchy for Interpretable Few-Shot Learning](https://mlsanity.com/paper/2203.01482/) (2022)
- [Semantics-driven Attentive Few-shot Learning over Clean and Noisy Samples](https://mlsanity.com/paper/2201.03043/) (2022)
- [The Lean Data Scientist: Recent Advances towards Overcoming the Data Bottleneck](https://mlsanity.com/paper/2211.07959/) (2022)
- [Compositional Prototypical Networks for Few-Shot Classification](https://mlsanity.com/paper/2306.06584/) (2023)
- [LPN: Language-guided Prototypical Network for few-shot classification](https://mlsanity.com/paper/2307.01515/) (2023)
- [Dual Adversarial Alignment for Realistic Support-Query Shift Few-shot Learning](https://mlsanity.com/paper/2309.02088/) (2023)
- [Improved Few-Shot Image Classification Through Multiple-Choice Questions](https://mlsanity.com/paper/2407.16145/) (2024)
- [Multi-Modal Fusion by Meta-Initialization](https://mlsanity.com/paper/2210.04843/) (2022)
- [Few Shot Class Incremental Learning using Vision-Language models](https://mlsanity.com/paper/2405.01040/) (2024)
- [On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization](https://mlsanity.com/paper/2405.18751/) (2024)
- [Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection](https://mlsanity.com/paper/2507.07994/) (2025)

## Abstract (arXiv)

Learning from one or few visual examples is one of the key capabilities of humans since early infancy, but is still a significant challenge for modern AI systems. While considerable progress has been achieved in few-shot learning from a few image examples, much less attention has been given to the verbal descriptions that are usually provided to infants when they are presented with a new object. In this paper, we focus on the role of additional semantics that can significantly facilitate few-shot visual learning. Building upon recent advances in few-shot learning with additional semantic information, we demonstrate that further improvements are possible by combining multiple and richer semantics (category labels, attributes, and natural language descriptions). Using these ideas, we offer the community new results on the popular miniImageNet and CUB few-shot benchmarks, comparing favorably to the previous state-of-the-art results for both visual only and visual plus semantics-based approaches. We also performed an ablation study investigating the components and design choices of our approach.