1 research outputs found
Hierarchical Prototype Learning for Zero-Shot Recognition
Zero-Shot Learning (ZSL) has received extensive attention and successes in
recent years especially in areas of fine-grained object recognition, retrieval,
and image captioning. Key to ZSL is to transfer knowledge from the seen to the
unseen classes via auxiliary semantic prototypes (e.g., word or attribute
vectors). However, the popularly learned projection functions in previous works
cannot generalize well due to non-visual components included in semantic
prototypes. Besides, the incompleteness of provided prototypes and captured
images has less been considered by the state-of-the-art approaches in ZSL. In
this paper, we propose a hierarchical prototype learning formulation to provide
a systematical solution (named HPL) for zero-shot recognition. Specifically,
HPL is able to obtain discriminability on both seen and unseen class domains by
learning visual prototypes respectively under the transductive setting. To
narrow the gap of two domains, we further learn the interpretable
super-prototypes in both visual and semantic spaces. Meanwhile, the two spaces
are further bridged by maximizing their structural consistency. This not only
facilitates the representativeness of visual prototypes, but also alleviates
the loss of information of semantic prototypes. An extensive group of
experiments are then carefully designed and presented, demonstrating that HPL
obtains remarkably more favorable efficiency and effectiveness, over currently
available alternatives under various settings.Comment: This manuscript has been accepted by IEEE Transactions on Multimedi