摘要:本文主要提出了一个深度特征流算法,用于视频的识别。它仅在稀疏的关键帧上运行计算量极大的卷积子网络,并通过流场将它们的深度特征图传输到其他帧。由于流计算方法相对较快,所以算法得到了明显的加速。整…
deep feature flow算法 文章中将目标检测或者语义分割网络分解成两个连续的子网络,NfeatNfeat是特征网络,一般用resnet,NtaskNtask是任务网络,在特征图上进行语义分割或者目标检测任务。 图中的F是光流估计网络,这里用的是改造过的flownet,输入相邻的两帧图片,得到和feature map大小一样的特征光流图,flownet已经在光流估...
Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition Abstract 1.提出了一个Optical Flow guided Feature(OFF)模块,可以用于video action recognition。直接理解,该模块可以直接计算deep fea... Flow-Guided Feature Aggregation for Video Object Detection论文笔记 ...
论文地址:https://arxiv.org/abs/1611.07715 开源代码:https://github.com/msracver/Deep-Feature-Flow 热门文章推荐
deepfeatureflowforvideorecognition 深度特征流 论文:https://arxiv.org/pdf/1611.07715.pdf 项目地址:Deep-Feature-Flow出发点是在视频中的深度特征的帧间差异比较小,而对于每一帧获取其深度特征的时间和计算的成本十分大,考虑将前特征跨越到当前帧。 1 选取一个Key frame(计算关键帧通过特征提取 ...
Deep Feature Flow can easily make use of sparsely annotated video recognition datasets, where only a small portion of the frames are annotated with ground-truth labels. Click image to watch our demo video This is an official implementation forDeep Feature Flow for Video Recognition(DFF) based on...
Deep Feature Flow for Video Recognition [cvpr17] [Microsoft Research] [pdf] [arxiv] [code] Flow-Guided Feature Aggregation for Video Object Detection [ax1708/iccv17] [pdf] [notes] Towards High Performance Video Object Detection [ax1711] [Microsoft] [pdf] [notes] RNN Online Video Object Det...
The solution to the problem of recognizing human actions on video sequences is one of the key areas on the path to the development and implementation of co... A Zelensky,V Voronin,M Zhdanova,... 被引量: 0发表: 2022年 Spatio-Temporal Deep Feature Fusion for Human Action Recognition Action...
论文笔记 之 Deep Feature Flow for Video Recognition 1,要解决的问题: 2,本文提出的方法: 3,网络结构: 3.1,特征计算网络Nfeat: 3.2,光流计算网络F: 3.3,利用光流传播高级特征: 3.4, key frame的选取策略: 3.5,任务网络Ntask: 3.5.1, 语义分割任务: 3.5.2, 目标检测任务: 3.6,算法伪代码: 4... ...
Efficient filter flow for space-variant multiframe blind deconvolution. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 607–614 (IEEE, 2010). Xie, Y. et al. Removing turbulence effect via hybrid total variation and deformation-guided kernel regression. IEEE Trans. Image ...