# Zero-Shot Object Detection by Hybrid Region Embedding

**Authors:** Berkan Demirel, Ramazan Gokberk Cinbis, Nazli Ikizler-Cinbis  
**arXiv:** [1805.06157](https://arxiv.org/abs/1805.06157)  
**Date:** 2018-05-16  
**Summary page:** https://www.summarizepaper.com/en/paper/1805.06157v1/

## Key points

- Authors present a novel approach for zero-shot object detection (ZSD) in computer vision
- ZSD refers to the scenario where no visual training data is available for some target object classes
- Proposed method uses a convex combination of embeddings and a detection framework to address this problem
- Method evaluated on a dataset created from Fashion-MNIST images and a custom zero-shot split for Pascal VOC detection challenge
- Experimental results demonstrate promising results for ZSD
- Object detection is a challenging task, made more difficult by not having visual training data for certain target object classes
- Proposed method utilizes multiple embeddings to enhance the ability to detect unseen objects
- Evaluation conducted on both real-world and synthetic data, showing potential applicability in detecting new or rare objects

## Summary

Authors Berkan Demirel, Ramazan Gokberk Cinbis, and Nazli Ikizler-Cinbis present a novel approach to tackle the challenging problem of zero-shot object detection (ZSD) in computer vision. ZSD refers to the scenario where no visual training data is available for some of the target object classes. The authors propose using a convex combination of embeddings in conjunction with a detection framework to address this problem. To evaluate their method, the authors construct a simple dataset from Fashion-MNIST images and also create a custom zero-shot split for the Pascal VOC detection challenge. The experimental results demonstrate that their approach yields promising results for ZSD. Object detection is known to be one of the most difficult tasks in computer vision as it requires accurate prediction of both object classes and their locations within images. However, ZSD introduces an additional level of complexity by not having any visual training data for certain target object classes. This makes it even more challenging to accurately detect these unseen objects. The proposed method addresses this challenge by utilizing a convex combination of embeddings along with a detection framework. Embeddings are representations that capture the semantic information about objects in an image. By combining multiple embeddings, the authors aim to enhance the ability to detect unseen objects. To assess the effectiveness of their approach, the authors create a dataset using Fashion-MNIST images and design a custom zero-shot split for the Pascal VOC detection challenge. This allows them to evaluate their method on both real-world and synthetic data. The experimental results indicate that their method shows promise for ZSD. It successfully detects unseen objects without any visual training data, demonstrating its potential applicability in real-world scenarios where new or rare objects need to be detected. In summary, this study presents a novel approach for zero-shot object detection by leveraging a convex combination of embeddings within a detection framework. The proposed method is evaluated on both synthetic and real-world datasets, showing promising results for ZSD.