Due to the powerful representation ability with multiple levels of abstraction, deep learning-based multimodal representation learning has attracted much attention in recent years. In this paper, we provided a comprehensive survey on deep multimodal representation learning which has never been concentrated ...
Deep learning (DL) has shown outstanding performance in detection of of iris, face, and finger vein traits from raw data and overcome many of the limitations of traditional algorithms. There is few established papers that involves multimodal biological fusion around deep learning, how to design ...
Deep Multimodal Learning A survey on recent advances and trends读书笔记,程序员大本营,技术文章内容聚合第一站。
Radu, V., et al.: Multimodal deep learning for activity and context recognition. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 157:1–157:27 (2018) Ramachandram, D., Taylor, G.W.: Deep multimodal learning: a survey on recent advances and trends. IEEE Signal Process. Mag. ...
Wang, “Deep multimodal representation learning: A survey,” IEEE Access, 2019. [62] B. P. Yuhas, M. H. Goldstein, and T. J. Sejnowski, “Integration of acoustic and visual speech signals using neural networks,” IEEE Communications Magazine, 1989. [63] A. A. Lazarus et al., ...
The success of deep learning has been a catalyst to solving increasingly complex machine-learning problems, which often involve multiple data modalities. We review recent advances in deep multimodal learning and highlight the state-of the art, as well as gaps and challenges in this active research...
Others have also demonstrated the efficacy of deep learning in discriminating AD from specific types of nADD18,19,20. However, clinical evaluation of persons presenting in a memory clinic involves consideration of multiple etiologies of cognitive impairment. Therefore, the ability to successfully ...
作者: Deep, Multimodal,Library, Learning,Survey, Network 摘要: Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple ...
多模态联邦学习(Multimodal Federated Learning, MMFL)是一种涉及到多个客户端的协作训练过程,每个客户端拥有不同的模态设置(类型)与数据,可以在不共享其本地原始数据的情况下执行学习(训练)任务。注:本仓库已经在Github开源 目录 Survey Unifying Achitectures Applications Multimodal Datasets 综述文章 TitleAuthorsLinks ...
The emergence of deep neural networks and the evolution of deep learning tools and techniques has led to the development of deep learning-based approaches to multimodal sentiment analysis to address its challenges and constraints. This paper is a comprehensive comparative survey of sentiment analysis ...