  As an important and challenging problem in computer vision, zero-shot
learning (ZSL) aims at automatically recognizing the instances from unseen
object classes without training data. To address this problem, ZSL is usually
carried out in the following two aspects: 1) capturing the domain distribution
connections between seen classes data and unseen classes data; and 2) modeling
the semantic interactions between the image feature space and the label
embedding space. Motivated by these observations, we propose a bidirectional
mapping based semantic relationship modeling scheme that seeks for crossmodal
knowledge transfer by simultaneously projecting the image features and label
embeddings into a common latent space. Namely, we have a bidirectional
connection relationship that takes place from the image feature space to the
latent space as well as from the label embedding space to the latent space. To
deal with the domain shift problem, we further present a transductive learning
approach that formulates the class prediction problem in an iterative refining
process, where the object classification capacity is progressively reinforced
through bootstrapping-based model updating over highly reliable instances.
Experimental results on three benchmark datasets (AwA, CUB and SUN) demonstrate
the effectiveness of the proposed approach against the state-of-the-art
approaches.
