  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.
