Deep-LK for Efficient Adaptive Object Tracking "链接:https://pan.baidu.com/s/1Hn-CVgiR7WV0jvaYBv5G_A 提取码:mp97" 用于高效自适应对象跟踪的Deep-LK方法 In this paper, we present a new approach for efficient regression-based object tracking. Our approach is closely r…

Learning Dynamic Memory Networks for Object Tracking  ECCV 2018Updated on 2018-08-05 16:36:30 Paper: arXiv version Code: https://github.com/skyoung/MemTrack (Tensorflow Implementation) [Note]This paper is developed based on Siamese Network and DNC(Na…

What is the most efficient way to deep clone an object in JavaScript? Reliable cloning using a library Since cloning objects is not trivial (complex types, circular references, function etc.), most major libraries provide function to clone objects. D…

这周看了一篇动态网格序列水印的论文,由于目前在网格序列上做水印的工作特别少,加之我所看的这篇论文中的叙述相对简洁,理解起来颇为困难.好在请教了博士师兄,思路明朗了许多,也就把这思路整理在此了. 论文作者提出了一种三维网格序列盲水印算法,在他们的算法中用到了小波分析.我对小波分析只有一个大概的了解,所以细节的理解上可能不尽正确,索性就不详细解释小波分解的知识了. 首先介绍论文中水印的产生: 此篇论文中的水印为 W= +1 或 W = -1,具体位数上嵌入+1还是 -1 作者没有做详细解释 然后介绍…

一.贡献 (1)提出一种针对RGB-D的新的运动分割算法 (2)运动分割采用矢量量化深度图像 (3)数据集测试,并建立RGB-D SLAM系统 二.Related work [1]R.K. Namdev, A. Kundu, K.M. Krishna, C. Jawahar, Motion segmentation of multiple objects from a freely moving monocular camera, in: Robotics and Automation(ICRA…

dynamic obj = Newtonsoft.Json.JsonConvert.DeserializeObject(json); string greeting = obj.greeting; RetInfo retinfo = JsonConvert.DeserializeObject<RetInfo>(text2); dynamic result = new System.Dynamic.ExpandoObject(); dynamic msg = JsonConvert.Deseri…

  Just like: dynamic paper = MockPaper(); LINQPad.Extensions.Dump(paper); //paper.Dump(); Use LINQPad.Extensions.Dump replace obj.Dump…

IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017. IEEE Computer Society 2017, ISBN 978-1-5386-1032-9 Oral Session 1 Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Corre…

CVPR2017 paper list Machine Learning 1 Spotlight 1-1A Exclusivity-Consistency Regularized Multi-View Subspace Clustering Xiaojie Guo, Xiaobo Wang, Zhen Lei, Changqing Zhang, Stan Z. Li Borrowing Treasures From the Wealthy: Deep Transfer Learning Thro…

3D惯导Lidar仿真 LiDAR-Inertial 3D Plane Simulator 摘要 提出了最*点*面表示的形式化方法,并分析了其在三维室内同步定位与映射中的应用.提出了一个利用最*点*面表示的无奇异*面因子,并在基于图的优化框架中证明了它与惯性预积测量的融合.所得到的LiDAR惯性三维*面SLAM(LIPS)系统在定制的LiDAR模拟器和实际实验中都得到了验证. I.介绍 准确.鲁棒的室内定位和映射是自动机器人许多应用的基本要求.室内环境通常是丰富的指令信息,如直线和*面,应加以利…