# A Large-scale Attribute Dataset for Zero-shot Learning

arXiv: https://arxiv.org/abs/1804.04314 · HTML: https://mlsanity.com/paper/1804.04314/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2018.

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

- [Incremental Residual Concept Bottleneck Models](https://mlsanity.com/paper/2404.08978/) (2024)
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- [CoLLIE: Continual Learning of Language Grounding from Language-Image Embeddings](https://mlsanity.com/paper/2111.07993/) (2021)
- [Revisiting Document Representations for Large-Scale Zero-Shot Learning](https://mlsanity.com/paper/2104.10355/) (2021)
- [Large-Scale Attribute-Object Compositions](https://mlsanity.com/paper/2105.11373/) (2021)
- [Efficient Human-in-the-loop System for Guiding DNNs Attention](https://mlsanity.com/paper/2206.05981/) (2022)
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- [An Integral Projection-based Semantic Autoencoder for Zero-Shot Learning](https://mlsanity.com/paper/2306.14628/) (2023)
- [Natural Perturbed Training for General Robustness of Neural Network Classifiers](https://mlsanity.com/paper/2103.11372/) (2021)
- [Explaining Domain Shifts in Language: Concept erasing for Interpretable Image Classification](https://mlsanity.com/paper/2503.18483/) (2025)
- [Matryoshka Concept Bottleneck Models](https://mlsanity.com/paper/2605.20612/) (2026)
- [CAT: Concept-level backdoor ATtacks for Concept Bottleneck Models](https://mlsanity.com/paper/2410.04823/) (2024)
- [Compositional Attribute Imbalance in Vision Datasets](https://mlsanity.com/paper/2506.14418/) (2025)
- [ZSpeedL -- Evaluating the Performance of Zero-Shot Learning Methods using Low-Power Devices](https://mlsanity.com/paper/2110.04535/) (2021)
- [SEER-ZSL: Semantic Encoder-Enhanced Representations for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2312.13100/) (2023)
- [Extracting Human Attention through Crowdsourced Patch Labeling](https://mlsanity.com/paper/2403.15013/) (2024)
- [I-CARE: Analysis of interference-related phenomena in a controllable, diverse and representative unlearning setting for text-to-image models](https://mlsanity.com/paper/2609.00003/) (2026)

## Abstract (arXiv)

Zero-Shot Learning (ZSL) has attracted huge research attention over the past few years; it aims to learn the new concepts that have never been seen before. In classical ZSL algorithms, attributes are introduced as the intermediate semantic representation to realize the knowledge transfer from seen classes to unseen classes. Previous ZSL algorithms are tested on several benchmark datasets annotated with attributes. However, these datasets are defective in terms of the image distribution and attribute diversity. In addition, we argue that the "co-occurrence bias problem" of existing datasets, which is caused by the biased co-occurrence of objects, significantly hinders models from correctly learning the concept. To overcome these problems, we propose a Large-scale Attribute Dataset (LAD). Our dataset has 78,017 images of 5 super-classes, 230 classes. The image number of LAD is larger than the sum of the four most popular attribute datasets. 359 attributes of visual, semantic and subjective properties are defined and annotated in instance-level. We analyze our dataset by conducting both supervised learning and zero-shot learning tasks. Seven state-of-the-art ZSL algorithms are tested on this new dataset. The experimental results reveal the challenge of implementing zero-shot learning on our dataset.