Domain Uncertainty Based On Information Theory for Cross-Modal Hash Retrieval
Wei Chen, Nan Pu, Yu Liu, Erwin M. Bakker, Michael S. Lew · 2019
Cross-modal hash retrieval has received considerable interest in the area of deep learning. Here hash codes of data of different modalities are learned where pair-wise loss functions control feature similarity in a shared embedding space. In this paper we improve on feature similarity by using Shannon's information entropy with respect to the modality information that is left in learning superior hash codes. We introduce a novel network for predicting the domain from the learned features while the protagonist network uses a loss function based on Shannon's information entropy to learn to maximize the domain uncertainty and therefore the information content. Additionally, according to the number of common labels between each similar image-text pair, we define a multi-level similarity matrix as supervisory information, which constrains all similar pairs with different weights. We show with extensive experiments that our novel approach to domain uncertainty leads to a cross-modal hash retrieval that outperforms the state-of-the-art.