时空动作检测 (spatio-temporal action detection) : 输入一段视频,不仅需要识别视频中动作出现的区间和对应的类别,还要在空间范围内用一个包围框 (bounding box)标记出人物的空间位置。 一、算法介绍 ACT (《Action Tubelet Detector for Spatio-Temporal Action Localization》) YOWO(《You Only Watch Once: A Unif...
时空动作检测 (spatio-temporal action detection) : 输入一段视频,不仅需要识别视频中动作出现的区间和对应的类别,还要在空间范围内用一个包围框 (bounding box)标记出人物的空间位置。 一、算法介绍 ACT (《Action Tubelet Detector for Spatio-Temporal Action Localization》) YOWO(《You Only Watch Once: A Unif...
Kollias. Spatiotemporal features for action recognition and salient event detection. Cognitive Computation, 3(1):167-184, 2011.Rapantzikos, K., Avrithis, Y. and Kollias, S.: Spatiotemporal features for action recognition and salient event detection, Cognitive Compu- tation, Vol.3, No.1, pp...
这篇论文是CVPR2018年的录取论文,主要讨论了时空卷积的几种网络结构,在Action Recognition 的几个标准数据集上也取得了媲美最好方法的效果。作者是FAIR的工作人员,其中包括Du Tran(C3D)作者,Heng Wang(iDT)作者和Yann LecCun等,可谓是大牛云集。论文可以在这里下载。这里大概介绍下论文中的内容,可以看作是原论文的...
Meanwhile, the spatial context and temporal information were not fully utilized and processed in some networks. In this paper, a novel three-stream network spatiotemporal attention enhanced features fusion network for action recognition is proposed. Firstly, features fusion stream which includes multi-...
Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition 时空初始图卷积网络用于基于骨骼的动作识别 CVPR2020 STIGCN 邻接矩阵的拓扑是建模输入骨骼相关性的关键因素。先前方法主要集中于图拓扑的设计/学习。但是一旦了解了拓扑,网络的每一层中将仅存在一个单比例特征和一个转换。已经...
Spatiotemporal Multiplier Networks for Video Action Recognition 2017CVPR Christoph Feichtenhofer: Abstract 加入恒等映射核来捕捉长期依赖。 Intro ST-ResNet:没有提供它的设计选择有系统的理由 重新考虑双流的结合,ResNet较为深入的增加了解这些技术是如何相互作
Research in action detection has grown in the recent years, as it plays a key role in video understanding. Modelling the interactions (either spatial or temporal) between actors and their context has proven to be essential for this task. While recent works use spatial features with aggregated te...
A Closer Look at Spatiotemporal Convolutions for Action Recognition. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018.) 根据论文中作者的描述,他提出的MCx、rMCx和(2+1)D,是2D与3D的Middle Ground,混合卷积可以用更少的参数量取得与3D相当的Performance。(2+1)D对时空表达做...
原文是:《STM: SpatioTemporal and Motion Encoding for Action Recognition》 最近一直在思考,2D网络就真的无法捕捉时序上的一些信息了吗?本文给了我的答案,时序上的信息是可以做到的。本文最大的特点在我看来就是用2D网络实现对视频很好的识别率,相信这样的文章具有很大的实际意义,在现阶段的实际问题中,企业用3D的...