GitHub - AHupuJR/EFNet: Event-based Fusion for Motion …
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 …
EFNet: Enhancement-Fusion Network for Semantic Segmentation
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 …
EFNet: A multitask deep learning network for simultaneous ...
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 …
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 …
推荐项目：EFNet - 动态去模糊的未来之眼 - CSDN博客
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 …
EFNet: estimation of left ventricular ejection fraction from cardiac ...
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 …
EFNet: Enhancement-Fusion Network for Semantic …
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. …
EF-Net: RGB-D-based Saliency Detection Using Information
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 …
EFNet: Enhancement-Fusion Network for Semantic Segmentation
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” …
EFNet：用于语义分割的增强融合网络,Pattern Recognition - X-MOL
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。