Zero-Shot Object Detection: Joint Recognition and Localization of Novel Concepts

By Shafin Rahman, Salman Hameed Khan, Fatih Murat Porikli. 2020

58 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jianfeng Gao
2. Jianwei Yang
3. Lijuan Wang
4. Zhe Gan
5. Chunyuan Li
6. Lu Yuan
7. Pengchuan Zhang
8. Mohit Bansal

In Computer Vision and Pattern Recognition

1. Jianfeng Gao
2. Luc Van Gool
3. Jianwei Yang
4. Dacheng Tao
5. Lu Yuan
6. Trevor Darrell
7. Lei Zhang
8. Ming-Hsuan Yang

In Radiology, Nuclear Medicine and Imaging

1. Jianwei Yang
2. Lu Yuan
3. Yixuan Li
4. Jianfeng Gao
5. Xiyang Dai
6. Lei Zhang
7. Yin Li

In Representation & Self-Supervised Learning

1. Jianfeng Gao
2. Jianwei Yang
3. Chunyuan Li
4. Lu Yuan
5. Jenq-Neng Hwang
6. Xuefeng Du
7. Yixuan Li
8. Yiwu Zhong

Citations in the corpus

• RegionCLIP: Region-based Language-Image Pretraining (2021, background): “Zeroshot object detection aims at detecting novel object classes that are not seen during detector training [2, 18, 38, 39, 60, 65].”
• Grounded Language-Image Pre-training (2021, methodology): “Our setting differs from zero-shot detection [1, 13, 43, 44, 63, 68], where some categories are defined as unseen/rare and not present in the training set.”
• VOS: Learning What You Don't Know by Virtual Outlier Synthesis (2022, background): “, 2020) or used auxiliary information (Rahman et al., 2020), such as class attributes to perform object detection on unseen data, which is different from our focus of OOD detection.”