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Hashing learns compact binary codes to store and retrieve massive dataefficiently. Particularly, unsupervised deep hashing is supported by powerfuldeep neural networks and has the desirable advantage of label independence. Itis a promising technique for scalable image retrieval. However, deep modelsintroduce a large number of parameters, which is hard to optimize due to thelack of explicit semantic labels and brings considerable training cost. As aresult, the retrieval accuracy and training efficiency of existing unsuperviseddeep hashing are still limited. To tackle the problems, in this paper, wepropose a simple and efficient \emph{Lightweight Augmented Graph NetworkHashing} (LAGNH) method with a two-pronged strategy. For one thing, we extractthe inner structure of the image as the auxiliary semantics to enhance thesemantic supervision of the unsupervised hash learning process. For another, wedesign a lightweight network structure with the assistance of the auxiliarysemantics, which greatly reduces the number of network parameters that needs tobe optimized and thus greatly accelerates the training process. Specifically,we design a cross-modal attention module based on the auxiliary semanticinformation to adaptively mitigate the adverse effects in the deep imagefeatures. Besides, the hash codes are learned by multi-layer message passingwithin an adversarial regularized graph convolutional network. Simultaneously,the semantic representation capability of hash codes is further enhanced byreconstructing the similarity graph.

Two-pronged Strategy: Lightweight Augmented Graph Network Hashing for Scalable Image Retrieval