# Attributes as Operators: Factorizing Unseen Attribute-Object Compositions

arXiv: https://arxiv.org/abs/1803.09851 · HTML: https://mlsanity.com/paper/1803.09851/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2018.

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

- [Learning Graph Embeddings for Compositional Zero-shot Learning](https://mlsanity.com/paper/2102.01987/) (2021)
- [Open World Compositional Zero-Shot Learning](https://mlsanity.com/paper/2101.12609/) (2021)
- [Learning to Predict Visual Attributes in the Wild](https://mlsanity.com/paper/2106.09707/) (2021)
- [Learning to Compose Soft Prompts for Compositional Zero-Shot Learning](https://mlsanity.com/paper/2204.03574/) (2022)
- [Disentangling Visual Embeddings for Attributes and Objects](https://mlsanity.com/paper/2205.08536/) (2022)
- [Siamese Contrastive Embedding Network for Compositional Zero-Shot Learning](https://mlsanity.com/paper/2206.14475/) (2022)
- [KG-SP: Knowledge Guided Simple Primitives for Open World Compositional Zero-Shot Learning](https://mlsanity.com/paper/2205.06784/) (2022)
- [Do Vision-Language Pretrained Models Learn Composable Primitive Concepts?](https://mlsanity.com/paper/2203.17271/) (2022)
- [Learning Attention Propagation for Compositional Zero-Shot Learning](https://mlsanity.com/paper/2210.11557/) (2022)
- [Relation-aware Compositional Zero-shot Learning for Attribute-Object Pair Recognition](https://mlsanity.com/paper/2108.04603/) (2021)
- [Zero-Shot Compositional Concept Learning](https://mlsanity.com/paper/2107.05176/) (2021)
- [3D Compositional Zero-shot Learning with DeCompositional Consensus](https://mlsanity.com/paper/2111.14673/) (2021)
- [Prompting Large Pre-trained Vision-Language Models For Compositional Concept Learning](https://mlsanity.com/paper/2211.05077/) (2022)
- [Multi-Task Learning of Object State Changes from Uncurated Videos](https://mlsanity.com/paper/2211.13500/) (2022)
- [Generating Compositional Color Representations from Text](https://mlsanity.com/paper/2109.10477/) (2021)
- [Reference-Limited Compositional Zero-Shot Learning](https://mlsanity.com/paper/2208.10046/) (2022)
- [Cross-modal Image Retrieval with Deep Mutual Information Maximization](https://mlsanity.com/paper/2103.06032/) (2021)
- [Multi-Attribute Open Set Recognition](https://mlsanity.com/paper/2208.06809/) (2022)
- [Deep Mixture of Experts via Shallow Embedding](https://mlsanity.com/paper/1806.01531/) (2018)
- [Make an Omelette with Breaking Eggs: Zero-Shot Learning for Novel Attribute Synthesis](https://mlsanity.com/paper/2111.14182/) (2021)

## Abstract (arXiv)

We present a new approach to modeling visual attributes. Prior work casts attributes in a similar role as objects, learning a latent representation where properties (e.g., sliced) are recognized by classifiers much in the way objects (e.g., apple) are. However, this common approach fails to separate the attributes observed during training from the objects with which they are composed, making it ineffectual when encountering new attribute-object compositions. Instead, we propose to model attributes as operators. Our approach learns a semantic embedding that explicitly factors out attributes from their accompanying objects, and also benefits from novel regularizers expressing attribute operators' effects (e.g., blunt should undo the effects of sharp). Not only does our approach align conceptually with the linguistic role of attributes as modifiers, but it also generalizes to recognize unseen compositions of objects and attributes. We validate our approach on two challenging datasets and demonstrate significant improvements over the state-of-the-art. In addition, we show that not only can our model recognize unseen compositions robustly in an open-world setting, it can also generalize to compositions where objects themselves were unseen during training.