多模态深度学习(英文名:Multimodal Deep Learning)是人工智能(AI)的一个子领域,其重点是开发能够同时处理和学习多种类型数据的模型。这些数据类型,或称模态,可以包括文本、图像、音频、视频和传感器数据等。通过结合这些不同的模式,多模态深度学习旨在创建更强大和多功能的人工智能系统,能够更好地理解、解释复杂的现实...
The “deep learning” era (2010s until …),促使多模态研究发展的关键促成因素有4个,1)新的大规模多模态数据集,2)GPU快速计算,3)强大的视觉特征抽取能力,4)强大的语言特征抽取能力。 表示学习三篇参考文献 Multimodal Deep Learning [ICML 2011] Multimodal Learning with Deep Boltzmann Machines [NIPS 2012] ...
[4]. The self-taught learning paradigm uses unlabeled data (not necessarily from the same distri- bution as the labeled data) to learn representations that improve performance on some task. While self-taught learning was first motivated with sparse coding, recent work on deep learning [5, 6...
近期在arXiv上发布出一本新的名为《Multimodal Deep Learning》,是德国的一个seminar里,好多人一起整理出来在multimodal领域里对SOTA的综述。全书272页,很综合的对这个方向的工作以及展望进行了完整的阐述,看了下,还可以,开源的书,也是免费的,推荐给大家。书的结构如下: Multimodal Deep Learning 感兴趣的朋友可以去...
Multimodal Learning 多模态学习试图对不同模态的数据组合进行建模,这在现实世界的应用中经常出现。联合数据的一个例子是将文本(通常表示为离散的字数向量)与由像素强度和注释标签组成的成像数据相结合。由于这些模式具有根本上不同的统计属性,将它们结合在一起是不容易的,这就是为什么需要专门的建模策略和算法。
we also constructed traditional machine learning models to predict cognitive status and AD status using the same set of features used for the deep learning model, and the results are presented ineandf, respectively. The heat maps show fifteen features with the highest mean absolute SHAP values obt...
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 modalities. We present a series of tasks for multimodal learning and ...
Deep, MultimodalLibrary, LearningSurvey, NetworkDBLPInternational Conference on Machine LearningMultimodal deep learning - Ngiam, Khosla, et al. - 2011 () Citation Context ...al., 2009; Tran et al., 2012). The Boltzmann distribution permits several types to be jointly modelled, thus making the...
Internal cross validation results for individual data modality to predict Alzheimer’s stage (a) Imaging results: deep learning prediction performs better than shallow learning predictions (b) EHR results: deep learning outperforms shallow models kNN and SVM and is comparable to decision trees and ran...
Multimodal Deep Learning is an exciting and rapidly evolving field that holds great potential for advancing computer vision and other areas of artificial intelligence. Through the integration of multiple modalities, including visual, textual, and auditory information, multimodal learning allows machines to ...