# Context-Aware Zero-Shot Recognition

arXiv: https://arxiv.org/abs/1904.09320 · HTML: https://mlsanity.com/paper/1904.09320/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2019.

## Built on by (most cited first, 12 of 12)

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- [The Tensor Brain: A Unified Theory of Perception, Memory and Semantic Decoding](https://mlsanity.com/paper/2109.13392/) (2021)
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- [A Survey of Deep Learning for Low-Shot Object Detection](https://mlsanity.com/paper/2112.02814/) (2021)
- [Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning](https://mlsanity.com/paper/2403.07078/) (2024)
- [A Novel Neural-symbolic System under Statistical Relational Learning](https://mlsanity.com/paper/2309.08931/) (2023)
- [DFS: A Diverse Feature Synthesis Model for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2103.10764/) (2021)
- [Multimodal Query-guided Object Localization](https://mlsanity.com/paper/2212.00749/) (2022)

## Abstract (arXiv)

We present a novel problem setting in zero-shot learning, zero-shot object recognition and detection in the context. Contrary to the traditional zero-shot learning methods, which simply infers unseen categories by transferring knowledge from the objects belonging to semantically similar seen categories, we aim to understand the identity of the novel objects in an image surrounded by the known objects using the inter-object relation prior. Specifically, we leverage the visual context and the geometric relationships between all pairs of objects in a single image, and capture the information useful to infer unseen categories. We integrate our context-aware zero-shot learning framework into the traditional zero-shot learning techniques seamlessly using a Conditional Random Field (CRF). The proposed algorithm is evaluated on both zero-shot region classification and zero-shot detection tasks. The results on Visual Genome (VG) dataset show that our model significantly boosts performance with the additional visual context compared to traditional methods.