Learning Spatiotemporal Features with 3D Convolutional Networks 用3D卷积网络学习时空特征 摘要 We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset. Our findings are ...
We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset. Our findings are three-fold: 1) 3D ConvNets are more suitable for spatiotemporal feature learning compared ...
3 Learning Features with 3D ConvNets 3.1. 3D convolution and pooling 我们相信 3D CNN 网络适合于时空特征的学习,和 2D CNN 网络相比,3D ConvNet 通过3D 卷积和 3D 池化 可以对时间信息进行建模。 我们的思路是先在一个小的数据库上寻找一个最优的 3D ConvNet 网络结构,然后再在一个大的数据库上进行验证。
因此,作者在本文主要是想通过深度3D卷积神经网络来学习一种视频通用表征,并验证在这个视频表征上,只需要结合简单的分类器就可以在不同的视频任务上取得不错的效果。 2 相关工作 作者列举了一些和视频表征相关的其他工作: Laptev and Lindeberg proposed spatio-temporal interest points (STIPs) by extending Harris ...
Learning Spatiotemporal Features with 3D ConvolutionalNetworks Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri 摘要 这篇文章提出在大规模监督视频数据集上训练深度3D卷积神经网络(3D ConvNets)。3D卷积网络的优势有三点:1)同2D卷积网络相比,3D卷积网络更适合时空特征的学习;2)3D卷积网...
论文: Learning Spatiotemporal Features with 3D Convolutional Networks 官方代码(caffe):http://vlg.cs.dartmouth.edu/c3d/ 由Facebook和Dartmouth学院提出 被ICCV2015收录 一、核心创新 网络全部使用3D卷积和3D池化 方便在不同的任务中使用,如动作识别、相同动作判断、动态场景识别等 ...
关于论文Learning Spatiotemporal Features with 3D Convolutional Networks的介绍 这篇论文提出了一个比较高效的C3D网络来提取视频的空间时间特征。相比于2D网络,3D网络能够 更好的提取特征,而且只需配合简单分类器就能够比当前多数已有算法取得更好的表现。
In this paper, we propose using supervised feature learning as a way to learn spatio-temporal features. More specifically, a modified hidden conditional random field is applied to learn two high-level features conditioned on a certain action label. Among them, the individual features can describe ...
We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset. Our findings are three-fold: 1) 3D ConvNets are more suitable
Learning Spatiotemporal Features for Infrared Action Recognition with 3D Convolutional Neural NetworksInfrared (IR) imaging has the potential to enable more robust action recognition systems compared to visible spectrum cameras due to lower ... Z Jiang,V Rozgic,S Adali 被引量: 1发表: 2017年 Dynami...