【摘要】本研究的目的是确定当前的视频数据集是否具有足够的数据来训练具有时空三维(3D)内核的深度卷积神经网络(CNNs)(也就是3D卷积神经网络)。近年来,3D CNNs在动作识别领域的性能水平有了显著提高。然而,到目前为止,传统的研究只探索了相对较浅的3D架构。我们研究了当前视频数据集中从较浅到很深的各种3D CNN的...
论文名称:Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet? 论文地址:https://arxiv.org/abs/1711.09577v2 1 Intro 在使用具有大量参数的深度CNN时,大规模数据集的使用及其重要,并且近年来CNN在计算机视觉领域的使用显著增大。ImageNet超过一百万张图像,为成功的基于视觉的算法创建作出巨...
2、3D卷积神经网络架构 文中的3D CNN架构包含一个硬连线hardwired层、3个卷积层、2个下采样层和一个全连接层。每个3D卷积核卷积的立方体是连续7帧,每帧patch大小是60x40,架构如下: 在第一层,我们应用了一个固定的hardwired的核去对原始的帧进行处理,产生多个通道的信息,然后对多个通道分别处理。最后再将所有通...
Interleaved 3D-CNNs for Joint Segmentation of Small-Volume Structures in Head and Neck CT Images-笔记 技术标签:论文笔记 传统分割: (1)Atlas based methods, (2)Statistical shape/appearance based methods (3)Classification based methods 论文方法: 1.调整窗宽窗位为[-200,200]。(肉眼可以观察软组织器官...
Although 3D Convolutional Neural Networks (CNNs) are essential for most learning based applications involving dense 3D data, their applicability is limited due to excessive memory and computational requirements. Compressing such networks by pruning therefore becomes highly desirable. However, pruning 3D ...
human experts have put more efforts into analyzing the importance of different components in 3D convolutional neural networks (3D CNNs) to design more powerful spatiotemporal learning backbones. Among many others, spatiotemporal fusion is one of the essentials. It controls how spatial and temporal ...
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data Chris chris-hzc.github.io/5 人赞同了该文章 目录 收起 全文思路 Prerequisite 1 Building - Equivariant Networks 2 Definition of Equivariant Linear Maps 2.1 Linear 2.2 Equivariant 3 Equivariant Kernel Basis 3.1 Subspace ...
Recently, several convolutional neural networks have been proposed not only for 2D images, but also for 3D and 4D volume segmentation. Nevertheless, due to the large data size of the latter, acquiring a sufficient amount of training annotations is much m
Leveraging the capabilities of ASTER image, band ratio (BR) images, and principal component analysis (PCA) alongside the power of 3D convolutional neural networks (3D-CNNs), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed ...
Convolutional Neural Networks (CNNs) require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. In this paper we show that the sample complexity of CNNs can be significantly improved by using 3D roto-translation group convolutions (G-Con...