Modality|Collaborative Transformer with Hybrid Feature ...

Modality-Collaborative Transformer with Hybrid Feature ... - arXiv

The crucial component of MCT is a novel attention-based encoder which concurrently extracts and dynamically balances the intra- and inter- ...

Modality-collaborative Transformer with Hybrid Feature ...

In this article, we propose a unified framework, Modality-Collaborative Transformer with Hybrid Feature Reconstruction (MCT-HFR), to address these issues.

A modality‐collaborative convolution and transformer hybrid network ...

Request PDF | A modality‐collaborative convolution and transformer hybrid network for unpaired multi‐modal medical image segmentation with ...

A modality-collaborative convolution and transformer hybrid network ...

... feature normalization parameters according to the input. Secondly, we propose a modality-invariant vision transformer (MIViT) module as the shared ...

zxpoqas123/MCT-HFR: Source code for the paper ... - GitHub

Source code for the paper "Modality-collaborative Transformer with Hybrid Feature Reconstruction for Robust Emotion Recognition". - zxpoqas123/MCT-HFR.

A modality‐collaborative convolution and transformer hybrid network ...

We propose a modality-collaborative convolution and transformer hybrid network (MCTHNet) using semi-supervised learning for unpaired multi-modal segmentation ...

Dual-attention transformer-based hybrid network for multi-modal ...

(2) Context Fusion Bridge is presented to remix the feature maps with multiple scales and construct their correlations. The experiments on ACDC, ...

Stochastic Windows Convolutional Transformer for Hybrid Modality ...

First, the effective spatial and spectral feature projection networks are built independently based on hybrid-modal heterogeneous data ...

Cross-modal collaborative feature representation via Transformer ...

This paper designs a multimodal counting network based on the multimodal transformer mixer to realize cross-modal collaborative feature representation.

Hybrid Transformer Based Feature Fusion for Self-Supervised ...

Our model fuses per-pixel local information learned using two fully convolutional depth encoders with global contextual information learned by a transformer ...

Multimodal Prompt Transformer with Hybrid Contrastive Learning for ...

To address these issues and fully utilize the features of each modality, we adopted the following strategies: first, deep emotion cues ...

Progressively Hybrid Transformer for Multi-Modal Vehicle Re ... - MDPI

The local region hybrider fuses the cropped regions to let regions of each modal bring local structural characteristics of all modalities, ...

Collaborative networks of transformers and convolutional neural ...

Propose TC-CoNet with a hybrid Transformer-CNN for 3D medical image segmentation. •. Design PPE to extract accurate 3D features with spatial position ...

Multimodal Transformer Fusion for Continuous Emotion Recognition

Modality-collaborative Transformer with Hybrid Feature Reconstruction for Robust Emotion Recognition ... ACM Trans. Multim. Comput. Commun. Appl. 2024. TLDR.

Hybrid CNN-Transformer Feature Fusion for Single Image Deraining

In this paper, we propose a lightweight Hybrid CNN-Transformer Feature Fusion Network (dubbed as HCT-FFN) in a stage-by-stage progressive manner.

Hybrid Attention-Aware Transformer Network Collaborative ...

However, existing methods do not adequately mine multiscale feature information and ignore the importance of multiscale feature alignment, ...

MEAformer: Multi-modal Entity Alignment Transformer for Meta ...

MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid ... modality. features. We take surface / vision modality information as the entity.

Multi-modal Entity Alignment Transformer for Meta Modality Hybrid

This paper introduces MEAformer, a mlti-modal entity alignment transformer approach for meta modality hybrid, which dynamically predicts the mutual correlation ...

An hybrid CNN-Transformer model based on multi-feature extraction ...

Afterwards, these two sets of classification predictions are combined using learnable attention weights per modality and per class. It allows us to interpret ...

Cross-modality representation learning from transformer for hashtag ...

To extract the image feature representations, a hybrid neural network architecture was adopted. In the first step, the preliminary feature ...