We introduce a new model - temporal deformable residual network (TDRN) - aimed at analyzing video intervals at multiple temporal scales for labeling video frames. Our TDRN computes two parallel temporal streams: i) Residual stream that analyzes video information at its full temporal resolution, and ii) Pooling/unpooling stream that captures ...

本文网络：为了克服这些限制，本文提出一个新的时域卷积模型，称作temporal deformable residual network (TDRN)。TDRN使用深度时域残差网络对每个视频帧分类。 残差网络 将CNN特征作为输入，计算时间维度上多个时间尺度的变形卷积。如图1所示，完成两个平行时间流的 ...

deformable residual network (TDRN) – aimed at analyzing video intervals at multiple temporal scales for labeling video frames. Our TDRN computes two parallel temporal streams: i) Residual stream that analyzes video information at its full temporal resolution, and ii) …

We currently only support PyTorch-0.4.0 and CUDA 8.0. ./make.sh is required for DeformableConv and COCO tools during installation. Our models are aviliable at GoogleDrive and BaiduYun. arXiv paper describes this project in detail.

This paper is about temporal segmentation of human actions in videos. We introduce a new model -- temporal deformable residual network (TDRN) -- aimed at analyzing video intervals at multiple temporal scales for labeling video frames.

2018年4月27日 · TDRN computes two processing streams: Residual stream (marked red) that analyzes video information at its full temporal resolution for precise action segmentation, and Pooling/unpooling stream...

In this paper, we propose the use of time-delay recurrent networks (TDRNs) as an alternative to these polynomials. For this, the TDRN is tuned based on a set of residual fault data.

stun和turn服务的作用主要处理打洞与转发，配合完成ICE协议。 如果失败将求助于TCP，使用turn转发两个端点的音视频数据，turn转发的是两个端点之间的音视频数据不是信令数据。 另外turn服务器转发的是数据流，很占用带宽和资源。 所面临的一个实际困难就是如何有效地穿透各种NAT（Network Address Translator）/FW (Fire Wall)的问题。 对此， SIP （会话初始化协议）以往的解决方法由ALGs（ (Application Layer Gateway Service)）、STUN、TURN等方式。 现 …

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We introduce a new model – temporal deformable residual network (TDRN) – aimed at analyzing video intervals at multiple temporal scales for labeling video frames.