Transductive Zero-Shot Hashing via Coarse-to-Fine Similarity Mining

Abstract

Zero-shot Hashing (ZSH) is to learn hashing models for novel/target classes without training data, which is an important and challenging problem. Most existing ZSH approaches exploit transfer learning via an intermediate shared semantic representations between the seen/source classes and novel/target classes. However, the hash functions learned from the source dataset may show poor performance when directly applied to the target classes due to the dataset bias. In this paper, we study the transductive ZSH, i.e., we have unlabeled data for novel classes. We put forward a simple yet efficient joint learning approach via coarse-to-fine similarity mining which transfers knowledges from source data to target data. It mainly consists of two building blocks in the proposed deep architecture: 1) a shared two-streams network to learn the effective common image representations. The first stream operates on the source data and the second stream operates on the unlabeled data. And 2) a coarse-to-fine module to transfer the similarities of the source data to the target data in a greedy fashion. It begins with a coarse search over the unlabeled data to find the images that most dissimilar to the source data, and then detects the similarities among the found images via the fine module. Extensive evaluation results on several benchmark datasets demonstrate that the proposed hashing method achieves significant improvement over the state-of-the-art methods.