With the recent renaissance of deep convolution neural networks, encouraging
breakthroughs have been achieved on the supervised recognition tasks, where
each class has sufficient training data and fully annotated training data.
However, to scale the recognition to a large number of classes with few or now
training samples for each class remains an unsolved problem. One approach to
scaling up the recognition is to develop models capable of recognizing unseen
categories without any training instances, or zero-shot recognition/ learning.
This article provides a comprehensive review of existing zero-shot recognition
techniques covering various aspects ranging from representations of models, and
from datasets and evaluation settings. We also overview related recognition
tasks including one-shot and open set recognition which can be used as natural
extensions of zero-shot recognition when limited number of class samples become
available or when zero-shot recognition is implemented in a real-world setting.
Importantly, we highlight the limitations of existing approaches and point out
future research directions in this existing new research area.

本文综述了现有零样本识别技术的方方面面，包括模型表示、数据集、评估方式等，并简要介绍了一些相关的识别任务，如一次性识别和开放集识别等。同时，我们也指出了现有方法的局限性以及未来的研究方向。