# A Unified approach for Conventional Zero-shot, Generalized Zero-shot and Few-shot Learning

arXiv: https://arxiv.org/abs/1706.08653 · HTML: https://mlsanity.com/paper/1706.08653/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2017.

## Built on by (most cited first, 28 of 28)

- [Zero-Shot Instance Segmentation](https://mlsanity.com/paper/2104.06601/) (2021)
- [Zero-Shot Learning on 3D Point Cloud Objects and Beyond](https://mlsanity.com/paper/2104.04980/) (2021)
- [Dual Progressive Prototype Network for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2111.02073/) (2021)
- [Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments](https://mlsanity.com/paper/2105.08149/) (2021)
- [Towards Unbiased Multi-label Zero-Shot Learning with Pyramid and Semantic Attention](https://mlsanity.com/paper/2203.03483/) (2022)
- [Relation-aware Compositional Zero-shot Learning for Attribute-Object Pair Recognition](https://mlsanity.com/paper/2108.04603/) (2021)
- [Learning Robust Visual-semantic Mapping for Zero-shot Learning](https://mlsanity.com/paper/2104.05668/) (2021)
- [Contributions of Shape, Texture, and Color in Visual Recognition](https://mlsanity.com/paper/2207.09510/) (2022)
- [ANALOGICAL -- A Novel Benchmark for Long Text Analogy Evaluation in Large Language Models](https://mlsanity.com/paper/2305.05050/) (2023)
- [Active Relation Discovery: Towards General and Label-aware Open Relation Extraction](https://mlsanity.com/paper/2211.04215/) (2022)
- [Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2101.03292/) (2021)
- [Boosting Zero-shot Learning via Contrastive Optimization of Attribute Representations](https://mlsanity.com/paper/2207.03824/) (2022)
- [A Gating Model for Bias Calibration in Generalized Zero-shot Learning](https://mlsanity.com/paper/2203.04195/) (2022)
- [Spatial Transcriptomics Analysis of Zero-shot Gene Expression Prediction](https://mlsanity.com/paper/2401.14772/) (2024)
- [Frustratingly Simple but Effective Zero-shot Detection and Segmentation: Analysis and a Strong Baseline](https://mlsanity.com/paper/2302.07319/) (2023)
- [On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey](https://mlsanity.com/paper/2408.04879/) (2024)
- [SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems](https://mlsanity.com/paper/2106.15681/) (2021)
- [Learning Aligned Cross-Modal Representation for Generalized Zero-Shot Classification](https://mlsanity.com/paper/2112.12927/) (2021)
- [An Iterative Co-Training Transductive Framework for Zero Shot Learning](https://mlsanity.com/paper/2203.16041/) (2022)
- [Bias-Eliminated Semantic Refinement for Any-Shot Learning](https://mlsanity.com/paper/2202.04827/) (2022)
- [Semantic Enhanced Knowledge Graph for Large-Scale Zero-Shot Learning](https://mlsanity.com/paper/2212.13151/) (2022)
- [Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting](https://mlsanity.com/paper/2305.13533/) (2023)
- [Introducing "Forecast Utterance" for Conversational Data Science](https://mlsanity.com/paper/2309.03877/) (2023)
- [Simple but Effective Unsupervised Classification for Specified Domain Images: A Case Study on Fungi Images](https://mlsanity.com/paper/2311.08995/) (2023)
- [Structure-Aware Feature Generation for Zero-Shot Learning](https://mlsanity.com/paper/2108.07032/) (2021)
- [On the Transferability of Visual Features in Generalized Zero-Shot Learning](https://mlsanity.com/paper/2211.12494/) (2022)
- [A Systematic Evaluation and Benchmark for Embedding-Aware Generative Models: Features, Models, and Any-shot Scenarios](https://mlsanity.com/paper/2302.04060/) (2023)
- [Artificial Intelligence and Deep Learning Algorithms for Epigenetic Sequence Analysis: A Review for Epigeneticists and AI Experts](https://mlsanity.com/paper/2504.03733/) (2025)

## Abstract (arXiv)

Prevalent techniques in zero-shot learning do not generalize well to other related problem scenarios. Here, we present a unified approach for conventional zero-shot, generalized zero-shot and few-shot learning problems. Our approach is based on a novel Class Adapting Principal Directions (CAPD) concept that allows multiple embeddings of image features into a semantic space. Given an image, our method produces one principal direction for each seen class. Then, it learns how to combine these directions to obtain the principal direction for each unseen class such that the CAPD of the test image is aligned with the semantic embedding of the true class, and opposite to the other classes. This allows efficient and class-adaptive information transfer from seen to unseen classes. In addition, we propose an automatic process for selection of the most useful seen classes for each unseen class to achieve robustness in zero-shot learning. Our method can update the unseen CAPD taking the advantages of few unseen images to work in a few-shot learning scenario. Furthermore, our method can generalize the seen CAPDs by estimating seen-unseen diversity that significantly improves the performance of generalized zero-shot learning. Our extensive evaluations demonstrate that the proposed approach consistently achieves superior performance in zero-shot, generalized zero-shot and few/one-shot learning problems.