Zero-shot learning (ZSL) has received increasing attention in recent years especially in areas of fine-grained object recognition, retrieval, and image captioning. The key to ZSL is to transfer knowledge from the seen to the unseen classes via auxiliary class attribute vectors. However, the popularly learned projection functions in previous works cannot generalize well since they assume the distribution consistency between seen and unseen domains at sample-level. Besides, the provided non-visual and unique class attributes can significantly degrade the recognition performance in semantic space. In this paper, we propose a simple yet effective convolutional prototype learning (CPL) framework for zero-shot recognition. By assuming distribution consistency at task-level, our CPL is capable of transferring knowledge smoothly to recognize unseen samples. Furthermore, inside each task, discriminative visual prototypes are learned via a distance based training mechanism. Consequently, we can perform recognition in visual space, instead of semantic space. An extensive group of experiments are then carefully designed and presented, demonstrating that CPL obtains more favorable effectiveness, over currently available alternatives under various settings.

零镜头学习（ZSL）近年来受到越来越多的关注，尤其是在细粒度对象识别，检索和图像字幕领域。ZSL的关键是通过辅助类属性向量将知识从可见的类转移到看不见的类。但是，先前作品中广为学习的投影函数无法很好地概括，因为它们假设样本级别的可见域和不可见域之间的分布一致性。此外，提供的非视觉和唯一的类属性可能会大大降低语义空间中的识别性能。在本文中，我们提出了一个简单而有效的卷积原型学习（CPL）框架用于零镜头识别。通过假设任务级别的分发一致性，我们的CPL能够顺利传递知识，以识别看不见的样本。此外，在每个任务中，通过基于距离的训练机制来学习有区别的视觉原型。因此，我们可以在视觉空间而不是语义空间中执行识别。然后精心设计和展示了一组广泛的实验，证明了CPL在各种环境下都比目前可用的替代品具有更好的效果。