Fully convolutional networkResidual blockResidual imageCondition random fieldFully convolutional networks (FCNs) have been efficiently applied in splicing localization. However, the existing FCN-based methods still have three drawbacks: (a) their performance in detecting image details is unsatisfactory; (b)...
摘要 This paper addresses the problem of estimating the depth map of a scene given a single RGB image. We propose a fully convolutional architecture, encompassing residual learning, to model the ambiguous mapping between monocular images and depth maps. In order to improve the output resolution, w...
The proposed fully convolutional regression method can obtain sub-pixel surface positions in a single feed forward propagation without any fully-connected layers (thus requiring fewer parameters than He et al. (2019b)). Our network has the benefit of: 1) being trained end-to-end; 2) improving...
论文: FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics 论文地址:https://arxiv.org/pdf/1612.05360 论文思想: FusionNet利用机器学习的最新进展,如语义分割(U-Net)和残差神经网络,新引入了基于累加的跳过连接,允许更深入的网络体系结构来实现更精确的分割。 论文...
Figure 2. Our network inference flow with convolutional self-attention blocks Figure 3 shows the structure and flow of the CSA module. The CSA blocks can differ in implementation but are designed to emulate the relational encoding process of self-attention. To achieve relational...
The depth predictions for this specific image (top right corner) were obtained with a pre-trained fully convolutional residual network (FCRN)11. Full size image The protein inter-residue distance prediction problem is to predict a pair-wise distance matrix (2D) from a protein sequence (one-...
This repository contains the CNN models trained for depth prediction from a single RGB image, as described in the paper "Deeper Depth Prediction with Fully Convolutional Residual Networks". The provided models are those that were used to obtain the results reported in the paper on the benchmark ...
S. Hong, J. Oh, H. Lee, and B. Han. Learning transferrable knowledge for semantic segmentation with deep convolutional neural network. In CVPR, 2016. The research of this topic has proceeded along three different dimensions: unsupervised adaptation, supervised adaptation and semi-supervised adaptati...
segmentation confidence calibration uncertainty estimation fully convolutional neural network 1. Introduction The clinical image division is important for a wide variety of applications, including anatomical modelling, cancer progress monitoring, surgical planning, and treatment evaluation [1]. Considering the ...
Image classification and bounding box approaches have been proposed in existing vision-based automated concrete crack detection methods using deep convolutional neural networks. The current study proposes a crack detection method based on deep fully convolutional network (FCN) for semantic segmentation on ...