2022年7月14日 · Our Event Fusion Network (EFNet) sets the new state of the art in motion deblurring, surpassing both the prior best-performing image-based method and all event-based methods with public implementations on the GoPro dataset (by up to 2.47dB) and on our REBlur dataset, even in extreme blurry conditions.

2021年10月1日 · In this paper, we design an enhancement-fusion network (EFNet), which aims at enhancing an input image for more diversified features to boost the following task of pixel-wise labeling. Specifically, the enhancement modules are trained to …

2024年9月1日 · The proposed EchoFused Network (EFNet) simultaneously performs LV segmentation and EF estimation on echocardiogram videos by performing cross-module fusion. EFNet comprises two modules: a segmentation module and a regression module. The objective of the segmentation module is to delineate the LV’s boundary by creating a pixel-wise mask.

2024年8月28日 · Methods: A single, fully automated multitask network, the EchoFused Network (EFNet) is introduced that simultaneously addresses both left ventricle segmentation and ejection fraction estimation tasks through cross-module fusion. Our proposed approach utilizes semi-supervised learning to estimate the ejection fraction

2024年6月19日 · EFNet是Lei Sun等研究人员在ECCV 2022上提出的，旨在利用事件相机的高时间分辨率特性来对抗运动模糊。 该网络设计巧妙地融合了事件数据与传统帧图像信息，通过跨模态注意力机制实现多尺度特征的有效结合，大大提升了去模糊效果，树立了GoPro和自建REBlur数据集 ...

2024年7月10日 · The proposed method, EFNet, utilizes cardiac ultrasound video images for end-to-end EF value prediction. Performance evaluation on the EchoNet-Dynamic dataset yielded a mean absolute error (MAE) of 3.7 and an R2 score of 0.82. Experimental results demonstrate that EFNet outperforms state-of-the-art techniques, providing accurate EF predictions.

In this paper, we design an enhancement-fusion network (EFNet), which aims at enhancing an input image for more diversified features to boost the following task of pixel-wise labeling. Specifically, the enhancement modules are trained to produce multiple enhanced images.

This paper presents a novel information extraction and fusion network (EFNet) for RGB-D based SOD by employing a Siamese network with an encoder-decoder structure. Extensive experiments on 6 widely acknowledged benchmark datasets demonstrate the superiority of the proposed EF-Net over 15 state-of-the-art RGBD-based SOD methods.

2021年10月1日 · We propose the EFNet which generates one new image by manipulating or enhancing the given input image to facilitate semantic segmentation in a flexible “add-on” manner. The EFNet and the segmentation network collaborate closely and can be trained end-to-end, resulting in improved segmentation results. •

在本文中，我们设计了一个增强融合网络（EFNet），旨在增强输入图像以获得更多样化的特征，以促进像素标记的后续任务。 具体而言，增强模块被训练以产生多个增强图像。