This paper proposes a multimodal deep learning architecture combining text and audio information to predict dementia, a disease which affects around 55 million people all over the world and makes them in some cases dependent people. The system was evaluated on the DementiaBank Pitt Corpus dataset, ...
(e.g.,theunvoicedconsonants/p/and/k/).Inthispaper,weexamine multimodallearningandhowtoemploydeeparchitecturestolearnmultimodalrepresentations. Multimodallearninginvolvesrelatinginformationfrommultiplesources.Forexample,imagesand 3-ddepthscansarecorrelatedatfirst-orderasdepthdiscontinuitiesoftenmanifestasstrongedges ...
In recent years, VQA has seen significant progress due to the use of deep learning techniques and architectures, particularly theTransformer architecture. The Transformer architecture was originally introduced for language processing tasks and has shown great success in VQA. ...
A novel CNN architecture for accurate early detection and classification of Alzheimer’s disease using MRI data Article Open access 12 February 2024 Deep-learning (DL) has shown tremendous potential for clinical decision support for a variety of diseases, including diabetic retinopathy1,2, cancers3...
To tackle this, we propose a novel scalable multimodal deep learning architecture using new nested structures that explicitly leverage deep features within or across modalities. This aims at making the early layers of the architecture ... L Fidon,W Li,Garcia-Peraza-Herrera, Luis C,... - Sprin...
We used two separate computer vision-based approaches to fit the data. First, we used the ResNet50 architecture to fine-tune the multi-class classification algorithm using RBG frames. Second, we used the ResNet50 architecture with the same fine-tuning strategy against optica...
Transformer models: Models like ViLBERT or VisualBERT extend the transformer architecture to multimodal data, enabling more sophisticated understanding and generation tasks. End-to-end learning: Efforts to develop models that learn directly from raw multimodal data, reducing the reliance on pre-trained ...
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...
This study aimed to develop a deep learning model to fuse high-dimensional MR image features and the clinical information for the pretreatment prediction of pCR to NAC in breast cancer. We designed the architecture of deep neural network that combined ResNet-50 with 3D CNNs for MR images and...
Neural-Symbolic Programming是将Neural-Symbolic与program synthesis结合起来,主要的目标是生成程序或者代码去解决特定的任务。与end2end的deep learning相比,神经符号编程具有多种优势: 程序有时可以自然地represent长期的过程性任务,但它们难以使用深度网络执行;