Correlated attribute transfer with multi-task graph-guided fusion

Due to the describable or human-nameable nature of visual attributes, the attribute-based methods have been receiving much attentions in recent years in many applications. The advantages of the utilization of visual attributes are that they can be composed to create descriptions at various levels of specificity or they can be learned once and then applied to recognize new objects or categories. Therefore, attribute prediction becomes an essential problem to boost image understanding. This paper proposes an approach for correlated attribute transfer from a well-defined source image set to an uncontrolled target image set for attribute prediction. We call it correlated attribute transfer with multi-task graph-guided fusion (CAT-MtG2F). The novelty of CAT-MtG2F is to encourage highly correlated attributes to share a common set of relevant low-level features and transfer the learned common structure from the source image set to the target image set. The experiments show that the proposed CAT-MtG2F achieves better performance in attribute prediction.