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Editorial to Special Issue on Deep Learning for Intelligent Multimedia Analytics

This special issue is highly motivated by the convergence of deep learning and multi-modal contents, since digital contents nowadays are inherently multimedia and the advancement of deep learning has greatly changed the landscape of multimedia content analysis. Multimedia data has been generated, published, and spread explosively, becoming an indispensable part of today’s big data. Such large-scale multimedia data has created challenges and opportunities for intelligent multimedia analysis. Meanwhile, with the recent advances in deep-learning techniques, we are now able to boost the intelligent multimedia analysis significantly. As a result, deeplearning−based intelligent multimedia analysis is becoming an emerging research area in the field of multimedia. The aim of this special issue is to call for a coordinated effort to understand the scenarios and challenges emerging in multimedia analysis with deep-learning techniques. The multimedia data of interest cover a wide spectrum, ranging from text, audio, image, and web videos to surveillance videos. Particularly, this special issue focuses on the visual domain, e.g., image and video analysis. Articles included cover a variety of topics, including representation learning, recognition, as well as real applications driven by deep-based techniques. The first article of this special issue, authored by the guest editors, presents a survey on deeplearning−based multimedia analytics, which starts by summarizing the fundamental deep-learning techniques and then reviews representative high-level tasks such as classification, detection, captioning, and segmentation. Progress on popular benchmarks gives a clear path of development and helps identify the milestone works and future directions. The following articles in this special issue introduce a variety of deep based multimedia analytics solutions. Most of the articles in this special issue address the problem of recognition and representation learning. The article titled “Convolutional Attention Networks for Scene Text Recognition,” by Shancheng Fang, Hongtao Xie, Yating Yang, Yan Li, and Yongdong Zhang, presents a convolutional attention network (CAN) for unconstrained scene text recognition based on a fully Convolutional Neural Network (CNN) architecture and the attention mechanism. The article “Structure-Aware Deep Learning for Product Image Classification,” by Zhineng Chen, Shanshan Ai, and Caiyan Jia, classifies product images by leveraging the hierarchical structure and attributes existing in online web stores. With these structural-aware clues, an advanced CNN model is developed beyond the one-versus-all classification as in conventional methods. The article “Deep Patch Representations with Shared Codebook for Scene Classification,” by Shuqiang Jiang, Gongwei Chen, Xinhang Song, and Linhu Liu, studies the scene classification by deep patch representations. Compared with general objects (e.g., product), scene images are more abstract. Object and scene images have different characteristics with different scales and composition structures. This article particularly studies how to integrate the local mid-level semantic representation, including both object and scene concepts, with shared codebooks. The article “Visual Content Recognition by Exploiting Semantic Feature Map with Attention and Multi-task Learning,” by Rui-wei Zhao, Zuxuan Wu, Jianguo Li, and Yu-gang Jiang, addresses