Deep Learning Techniques for Future Intelligent Cross-Media Retrieval

Abstract

With the advancement in technology and the expansion of broadcasting, cross-media retrieval has gained much attention. It plays a significant role in big data applications and consists in searching and finding data from different types of media. In this paper, we provide a novel taxonomy according to the challenges faced by multi-modal deep learning approaches in solving cross-media retrieval, namely: representation, alignment, and translation. These challenges are evaluated on deep learning (DL) based methods, which are categorized into four main groups: 1) unsupervised methods, 2) supervised methods, 3) pairwise based methods, and 4) rank based methods. Then, we present some well-known cross-media datasets used for retrieval, considering the importance of these datasets in the context in of deep learning based cross-media retrieval approaches. Moreover, we also present an extensive review of the state-of-the-art problems and its corresponding solutions for encouraging deep learning in cross-media retrieval. The fundamental objective of this work is to exploit Deep Neural Networks (DNNs) for bridging the "media gap", and provide researchers and developers with a better understanding of the underlying problems and the potential solutions of deep learning assisted cross-media retrieval. To the best of our knowledge, this is the first comprehensive survey to address cross-media retrieval under deep learning methods.

arXiv admin note: text overlap with arXiv:1804.09539 by other authors

References in corpus (25)

• Deep Learning in Neural Networks: An Overview
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Distributed Representations of Words and Phrases and their Compositionality
• A Brief Survey of Deep Reinforcement Learning
• DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
• Microsoft COCO Captions: Data Collection and Evaluation Server
• Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
• Zero-Shot Learning Through Cross-Modal Transfer
• Deep Fragment Embeddings for Bidirectional Image Sentence Mapping
• FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics
• A Comprehensive Survey on Cross-modal Retrieval
• Language Models for Image Captioning: The Quirks and What Works
• Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks
• Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces
• Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks
• Multi-column Deep Neural Networks for Image Classification
• On Deep Multi-View Representation Learning: Objectives and Optimization
• Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech
• HashGAN:Attention-aware Deep Adversarial Hashing for Cross Modal Retrieval
• NeuRec: On Nonlinear Transformation for Personalized Ranking
• DeepRec: An Open-source Toolkit for Deep Learning based Recommendation
• MARS: Memory Attention-Aware Recommender System
• Cross-media Multi-level Alignment with Relation Attention Network
• Towards an All-Purpose Content-Based Multimedia Information Retrieval System
• Water Preservation in Soan River Basin using Deep Learning Techniques