A Survey Of zero shot detection: Methods and applications
Chufeng Tan, Xing Xu, Fumin Shen
Abstract
Zero shot learning (ZSL) is aim to identify objects whose label is unavailable during training. This learning paradigm makes classifier has the ability to distinguish unseen class. The traditional ZSL method only focuses on the image recognition problems that the objects only appear in the central part of images. But real-world applications are far from ideal, which images can contain various objects. Zero shot detection (ZSD) is proposed to simultaneously localizing and recognizing unseen objects belongs to novel categories. We propose a detailed survey about zero shot detection in this paper. First, we summarize the background of zero shot detection and give the definition of zero shot detection. Second, based on the combination of traditional detection framework and zero shot learning methods, we categorize existing zero shot detection methods into two different classes, and the representative methods under each category are introduced. Third, we discuss some possible application scenario of zero shot detection and we propose some future research directions of zero-shot detection.