Low-rank embedded orthogonal subspace learning for zero-shot classification

Abstract

Zero-shot classification methods have attracted considerable attention in recent years. Existing ZSC methods encounter domain shift, hubness and visual–semantic gap problems. To address these problems, we propose a low-rank embedded orthogonal subspace learning method (LEOSL) for ZSC. Many previous works project visual features to the semantic space. However, they often suffer from the visual–semantic gap problem. To handle this problem, we project the visual representations and semantic representations to the common subspace. To address the domain shift problem, we restrict the mapping functions with a low-rank constraint. To handle the hubness problem, we introduce the class similarity term so that samples of the same class are located near each other, while samples of different classes are located far away. Furthermore, we restrict the shared representations in the subspace with an orthogonal constraint to remove the correlation between samples. The results show the superiority of LEOSL compared to many state-of-the-art methods.