Unsupervised deep quantization for object instance search

Abstract

In this paper, we propose an unsupervised deep quantization (UDQ) method for object instance search. The UDQ utilizes product quantization to discover the underlying self-supervision information of the training data and iteratively exploits the self-supervision information to optimize features of the training data in an unsupervised fashion. The optimized features are further used to update the self-supervision information for the subsequent training procedure. We introduce two constraints, the separability constraint and the discriminability constraint, to encourage the features to satisfy a cluster structure which is essential for the effective supervision information generation with the product quantization. The UDQ is optimized with an iterative optimization strategy which guarantees that the features and the supervision information can be enhanced each other alternately in a unified model. Moreover, we develop three refinement strategies to refine features to obtain better supervision information for the model optimization. Experimental results on four datasets show the superiority of our UDQ over the state-of-the-art methods.