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Found 10 papers, 5 papers with code
TNF: Tri-branch Neural Fusion for Multimodal Medical Data Classification
This paper presents a Tri-branch Neural Fusion (TNF) approach designed for classifying multimodal medical images and tabular data.
Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering Pairs
This paper presents an in-depth study of multimodal machine translation (MMT), examining the prevailing understanding that MMT systems exhibit decreased sensitivity to visual information when text inputs are complete.
PartialFormer: Modeling Part Instead of Whole
The design choices in Transformer feed-forward neural networks have resulted in significant computational and parameter overhead.
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Machine Translation
on WMT2014 English-German
EIT: Enhanced Interactive Transformer
In this paper, we propose a novel architecture, the Enhanced Interactive Transformer (EIT), to address the issue of head degradation in self-attention mechanisms.
Learning Multiscale Transformer Models for Sequence Generation
In this work, we define those scales in different linguistic units, including sub-words, words and phrases.
COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty
We utilize the scale uncertainty among various receptive field sizes of a segmentation FCN to obtain infection regions.
Micro CT Image-Assisted Cross Modality Super-Resolution of Clinical CT Images Utilizing Synthesized Training Dataset
Unsupervised SR methods are required that do not need paired LR and HR images.
Super-resolution of clinical CT volumes with modified CycleGAN using micro CT volumes
This paper presents a super-resolution (SR) method with unpaired training dataset of clinical CT and micro CT volumes.
Multi-modality super-resolution loss for GAN-based super-resolution of clinical CT images using micro CT image database
This paper newly introduces multi-modality loss function for GAN-based super-resolution that can maintain image structure and intensity on unpaired training dataset of clinical CT and micro CT volumes.
DeepIlluminance: Contextual Illuminance Estimation via Deep Neural Networks
First, the contextual net with a center-surround architecture extracts local contextual features from image patches, and generates initial illuminant estimates and the corresponding color corrected patches.
