Cross-Media Fine-Grained Representation Learning Based on Multi-modal Graph and Adversarial Hash Attention Network
LIANG Meiyu1, WANG Xiaoxiao1, DU Junping1
1. Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science(National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876
Abstract：There are problems of feature heterogeneity and semantic gap between data of different media types in cross-media data search, and social network data often exhibits semantic sparsity and diversity. Aiming at these problems, a cross-media fine-grained representation learning model based on multi-modal graph and adversarial Hash attention network(CMFAH) is proposed to obtain a unified cross-media semantic representation and applied to social network cross-media search. Firstly, an image-word association graph is constructed, and direct and implicit semantic associations between image and text words are mined based on the graph random walk strategy to expand the semantic relationship. A cross-media fine-grained feature learning network based on cross-media attention is constructed, and the fine-grained semantic association between images and texts is learned collaboratively through the cross-media attention mechanism. A cross-media adversarial hash network is constructed, and an efficient and compact cross-media unified hash semantic representation is obtained by the joint cross-media fine-grained semantic association learning and adversarial hash learning. Experimental results show that CMFAH achieves better cross-media search performance on two benchmark cross-media datasets.
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2022, Vol. 35 
