A semi-supervised zero-shot image classification method based on soft-target

Zero-shot learning (ZSL) aims at training a classification model with data only from seen categories to recognize data from disjoint unseen categories. Domain shift and generalization capability are two fundamental challenges in ZSL. In this paper, we address them with a novel Soft-Target Semi-supervised Classification (STSC) model. Specifically, an autoencoder network is leveraged, where both labeled seen data from the seen categories and unlabeled ancillary data collected from Internet or other datasets are employed as two branches, respectively. For the branch of labeled seen data, side information are employed as the latent vectors to separately connect the input of encoder and the output of decoder. In this way, visual and side information are implicitly aligned. For the branch of unlabeled ancillary data, it explicitly strengthens the reconstruction ability of the network. Meanwhile, these ancillary data can be viewed as a smooth to the domain distribution, which contributes to the alleviation of the domain shift problem. To further guarantee the generation ability, a Softmax-T loss function is proposed by making full use of the soft target. Extensive experiments on three benchmark datasets show the superiority of the proposed approach under tasks of both traditional zero-shot learning and generalized zero-shot learning.