Zero-Shot Recognition using Dual Visual-Semantic Mapping Paths

Understand

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.