# Recovering the Missing Link: Predicting Class-Attribute Associations for Unsupervised Zero-Shot Learning

arXiv: https://arxiv.org/abs/1610.04787 · HTML: https://mlsanity.com/paper/1610.04787/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2016.

## Built on by (most cited first, 20 of 20)

- [Decoupling Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/2112.07910/) (2021)
- [Learning to Predict Visual Attributes in the Wild](https://mlsanity.com/paper/2106.09707/) (2021)
- [Zero Experience Required: Plug & Play Modular Transfer Learning for Semantic Visual Navigation](https://mlsanity.com/paper/2202.02440/) (2022)
- [Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds](https://mlsanity.com/paper/2108.06230/) (2021)
- [Opening up Open-World Tracking](https://mlsanity.com/paper/2104.11221/) (2021)
- [Open-vocabulary Attribute Detection](https://mlsanity.com/paper/2211.12914/) (2022)
- [VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot Learning](https://mlsanity.com/paper/2203.10444/) (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)
- [SGNet: A Super-class Guided Network for Image Classification and Object Detection](https://mlsanity.com/paper/2104.12898/) (2021)
- [On Leveraging Variational Graph Embeddings for Open World Compositional Zero-Shot Learning](https://mlsanity.com/paper/2204.11848/) (2022)
- [ZEETAD: Adapting Pretrained Vision-Language Model for Zero-Shot End-to-End Temporal Action Detection](https://mlsanity.com/paper/2311.00729/) (2023)
- [Large-Scale Attribute-Object Compositions](https://mlsanity.com/paper/2105.11373/) (2021)
- [Field-Guide-Inspired Zero-Shot Learning](https://mlsanity.com/paper/2108.10967/) (2021)
- ["Zero-Shot" Point Cloud Upsampling](https://mlsanity.com/paper/2106.13765/) (2021)
- [Towards Automated Knowledge Integration From Human-Interpretable Representations](https://mlsanity.com/paper/2402.16105/) (2024)
- [ArtVLM: Attribute Recognition Through Vision-Based Prefix Language Modeling](https://mlsanity.com/paper/2408.04102/) (2024)
- [End-to-end Generative Zero-shot Learning via Few-shot Learning](https://mlsanity.com/paper/2102.04379/) (2021)
- [Instance Adaptive Prototypical Contrastive Embedding for Generalized Zero Shot Learning](https://mlsanity.com/paper/2309.06987/) (2023)
- [Adapting to the Unknown: Robust Meta-Learning for Zero-Shot Financial Time Series Forecasting](https://mlsanity.com/paper/2504.09664/) (2025)

## Abstract (arXiv)

Collecting training images for all visual categories is not only expensive but also impractical. Zero-shot learning (ZSL), especially using attributes, offers a pragmatic solution to this problem. However, at test time most attribute-based methods require a full description of attribute associations for each unseen class. Providing these associations is time consuming and often requires domain specific knowledge. In this work, we aim to carry out attribute-based zero-shot classification in an unsupervised manner. We propose an approach to learn relations that couples class embeddings with their corresponding attributes. Given only the name of an unseen class, the learned relationship model is used to automatically predict the class-attribute associations. Furthermore, our model facilitates transferring attributes across data sets without additional effort. Integrating knowledge from multiple sources results in a significant additional improvement in performance. We evaluate on two public data sets: Animals with Attributes and aPascal/aYahoo. Our approach outperforms state-of-the-art methods in both predicting class-attribute associations and unsupervised ZSL by a large margin.