Adaptively Unified Semi-supervised Learning for Cross-Modal Retrieval

Abstract

Motivated by the fact that both relevancy of class labels and unlabeled data can help to strengthen multi-modal correlation, this paper proposes a novel method for cross-modal retrieval. To make each sample moving to the direction of its relevant label while far away from that of its irrelevant ones, a novel dragging technique is fused into a unified linear regression model. By this way, not only the relation between embedded features and relevant class labels but also the relation between embedded features and irrelevant class labels can be exploited. Moreover, considering that some unlabeled data contain specific semantic information, a weighted regression model is designed to adaptively enlarge their contribution while weaken that of the unlabeled data with non-specific semantic information. Hence, unlabeled data can supply semantic information to enhance discriminant ability of classifier. Finally, we integrate the constraints into a joint minimization formulation and develop an efficient optimization algorithm to learn a discriminative common subspace for different modalities. Experimental results on Wiki, Pascal and NUS-WIDE datasets show that the proposed method outperforms the state-of-the-art methods even when we set 20% samples without class labels.