Stochastic Windows Convolutional Transformer for Hybrid Modality ...

Convolutional neural network - Wikipedia

... transformer. Vanishing gradients and exploding gradients, seen during ... Higher-layer features are extracted from wider context windows, compared to lower-layer ...

Enhancing Fine-Grained 3D Object Recognition Using Hybrid Multi ...

In this paper, we propose a hybrid multi-modal Vision Transformer. (ViT) and Convolutional Neural Networks (CNN) approach to improve the performance of fine ...

Explainable hybrid vision transformers and convolutional network for ...

Magnetic resonance imaging (MRI) is the preferred modality for the evaluation of intra-axial, identification of normal brain structures, ...

Progressively Hybrid Transformer for Multi-Modal - ProQuest

... Microsoft OneDrive and ... convolutional neural network (CNN) branches. ... Consequently, combing the multi-modal hybrid transformer and the random hybrid ...

cmhungsteve/Awesome-Transformer-Attention - GitHub

BinaryViT: "BinaryViT: Pushing Binary Vision Transformers Towards Convolutional Models", CVPRW, 2023 (Huawei). ... H-DETR: "DETRs with Hybrid Matching", CVPR, ...

Enhancing Skin Cancer Diagnosis Using Swin Transformer with ...

In this study, we enhanced the Swin Transformer architecture by implementing the hybrid shifted window-based multi-head self-attention (HSW-MSA)

The Fully Convolutional Transformer for Medical Image Segmentation

We propose a novel transformer, capable of segment- ing medical images of varying modalities. Challenges posed by the fine-grained nature of medical image ...

Aman's AI Journal • Papers List

Jamba: A Hybrid Transformer-Mamba Language Model; Fine Tuning vs ... from McGill and Microsoft in 2021 proposes the Convolutional vision Transformer ...

MmSTCT: spatial–temporal convolution transformer network ...

In dynamic and dense driving environments, multi-modal trajectory prediction is often reduced to a single modality due to the inherent randomness and ...

Model Zoo - Deep learning code and pretrained models for transfer ...

ModelZoo curates and provides a platform for deep learning researchers to easily find code and pre-trained models for a variety of platforms and uses.

Convolution-free Referring Image Segmentation Using Transformers

Several studies employ transformers for an atten- tion module in/on CNNs as a CNN-transformer hybrid net- ... Convolutional random walk networks for semantic im-.

A unified hybrid transformer for joint MRI sequences super ... - OUCI

Abstract Objective. High-resolution multi-modal magnetic resonance imaging (MRI) is crucial in clinical practice for accurate diagnosis and treatment.

Understanding the brain with attention: A survey of transformers in ...

proposed a multi-modal medical TransFormer (mmFormer) that combined four hybrid modality ... Multiscale convolutional transformer for EEG ...

MCV-UNet: a modified convolution & transformer hybrid encoder ...

A modified convolution & transformer hybrid encoder-decoder network with multi-scale information fusion for ultrasound image semantic segmentation.

Review of deep learning: concepts, CNN architectures, challenges ...

... hybrid deep convolutional neural network model. Electronics. 2020;9 ... convolutional neural networks and conditional random field ...

Application of novel hybrid deep learning architectures combining ...

... model for engineering based on a convolutional neural network (CNN). ... Proposed conceptual model 3: hierarchical spatio-temporal transformer CNN-RNN hybrid ...

Swin Unet3D: a three-dimensional medical image segmentation ...

TransUnet [21] and TransBTS [30] are a kind of hybrid model in combining CNN and Transformer, using successive convolutional layers and ...

Half-hourly electricity price prediction with a hybrid convolution ...

Half-hourly electricity price prediction with a hybrid convolution neural network-random vector functional link deep learning approach.

The Fully Convolutional Transformer for Medical Image Segmentation

We propose a novel transformer, capable of segment- ing medical images of varying modalities. Challenges posed by the fine-grained nature of medical image ...

Transformers in Vision: A Survey - ACM Digital Library

In this sense, the hybrid designs tend to combine the strengths of both convolution and transformer models. ... transformer using shifted windows ...