Zero-shot recognition aims to accurately recognize objects of unseen classes
by using a shared visual-semantic mapping between the image feature space and
the semantic embedding space. This mapping is learned on training data of seen
classes and is expected to have transfer ability to unseen classes. In this
paper, we tackle this problem by exploiting the intrinsic relationship between
the semantic space manifold and the transfer ability of visual-semantic
mapping. We formalize their connection and cast zero-shot recognition as a
joint optimization problem. Motivated by this, we propose a novel framework for
zero-shot recognition, which contains dual visual-semantic mapping paths. Our
analysis shows this framework can not only apply prior semantic knowledge to
infer underlying semantic manifold in the image feature space, but also
generate optimized semantic embedding space, which can enhance the transfer
ability of the visual-semantic mapping to unseen classes. The proposed method
is evaluated for zero-shot recognition on four benchmark datasets, achieving
outstanding results.

本研究旨在通过利用语义空间流形与视觉 - 语义映射之间的内在关系来解决零样本识别问题，并提出了一个包含两条双重视觉 - 语义映射路径的框架，可以提高视觉 - 语义映射的迁移能力，并在四个基准数据集上取得了优异的结果。