# Joint Dictionaries for Zero-Shot Learning

arXiv: https://arxiv.org/abs/1709.03688 · HTML: https://mlsanity.com/paper/1709.03688/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2017.

## Built on by (most cited first, 9 of 9)

- [Detection and Continual Learning of Novel Face Presentation Attacks](https://mlsanity.com/paper/2108.12081/) (2021)
- [Identification of Novel Classes for Improving Few-Shot Object Detection](https://mlsanity.com/paper/2303.10422/) (2023)
- [Improved Region Proposal Network for Enhanced Few-Shot Object Detection](https://mlsanity.com/paper/2308.07535/) (2023)
- [Task-Aware Meta Learning-based Siamese Neural Network for Classifying Obfuscated Malware](https://mlsanity.com/paper/2110.13409/) (2021)
- [Exploring the Effect of Sparse Recovery on the Quality of Image Superresolution](https://mlsanity.com/paper/2308.02714/) (2023)
- [Cross-Domain Distribution Alignment for Segmentation of Private Unannotated 3D Medical Images](https://mlsanity.com/paper/2410.09210/) (2024)
- [Hyperparameter Analysis for Derivative Compressive Sampling](https://mlsanity.com/paper/2108.04355/) (2021)
- [Robust Internal Representations for Domain Generalization](https://mlsanity.com/paper/2309.15522/) (2023)
- [Cross-domain Few-shot Object Detection with Multi-modal Textual Enrichment](https://mlsanity.com/paper/2502.16469/) (2025)

## Abstract (arXiv)

A classic approach toward zero-shot learning (ZSL) is to map the input domain to a set of semantically meaningful attributes that could be used later on to classify unseen classes of data (e.g. visual data). In this paper, we propose to learn a visual feature dictionary that has semantically meaningful atoms. Such dictionary is learned via joint dictionary learning for the visual domain and the attribute domain, while enforcing the same sparse coding for both dictionaries. Our novel attribute aware formulation provides an algorithmic solution to the domain shift/hubness problem in ZSL. Upon learning the joint dictionaries, images from unseen classes can be mapped into the attribute space by finding the attribute aware joint sparse representation using solely the visual data. We demonstrate that our approach provides superior or comparable performance to that of the state of the art on benchmark datasets.