Learning Social Image Embedding with Deep Multimodal Attention Networks

TL;DR

This paper introduces DMAN, a deep multimodal attention network that jointly embeds social images by capturing both multimodal content relations and social network links, improving classification and search tasks.

Contribution

The paper proposes a novel deep model combining multimodal attention and Siamese-Triplet networks for social image embedding, integrating content and link information.

Findings

DMAN outperforms state-of-the-art embeddings in classification.

DMAN significantly improves cross-modal search results.

The approach effectively captures fine-grained content relations.

Abstract

Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and cross-modal search. However, for social images which contain both link information and multimodal contents (e.g., text description, and visual content), simply employing the embedding learnt from network structure or data content results in sub-optimal social image representation. In this paper, we propose a novel social image embedding approach called Deep Multimodal Attention Networks (DMAN), which employs a deep model to jointly embed multimodal contents and link information. Specifically, to effectively capture the correlations between multimodal contents, we propose a multimodal attention network to encode the fine-granularity relation between image regions and textual words. To leverage the network structure for embedding learning, a novel Siamese-Triplet neural network is proposed to model the links among images. With the joint deep model, the learnt embedding can capture both the multimodal contents and the nonlinear network information. Extensive experiments are conducted to investigate the effectiveness of our approach in the applications of multi-label classification and cross-modal search. Compared to state-of-the-art image embeddings, our proposed DMAN achieves significant improvement in the tasks of multi-label classification and cross-modal search.