机器视觉通常用于分析图像,并生成一个对被生成图像物体或场景的描述,这些描述最终用于辅助或决定机器人控制决策。 一门基于计算机图像识别和分析的技术。主要用于自动检测,流程控制或机器人引导等。

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vGraph:联合社区检测和节点表示学习的生成模型,vGraph: A Generative Model for Joint Community Detection and Node Representational Learning

论文简介

本文研究了图分析的两个基本任务:社区检测和节点表示学习,它们分别捕获图的全局和局部结构。在目前的文献中,这两个任务通常是独立研究的,而实际上是高度相关的。我们提出了一个概率生成模型vGraph来协同学习社区成员和节点表示。具体地说,我们假设每个节点可以表示为一个社区的混合体,并且每个社区被定义为节点上的多项式分布。混合系数和社团分布通过节点和社区的低维表示来参数化。设计了一种有效的变分推理算法,使邻域节点在潜在空间中的隶属度趋于一致。在多个真实世界图上的实验结果表明,vGraph在社区检测和节点表示学习方面都非常有效,在这两个任务上都优于许多竞争基线。我们表明,该框架是非常灵活的,可以很容易地扩展到检测层次社区。

论文亮点

本文提出了一种新的学习节点表示的方法,同时利用变分推理的概念建立生成模型,对图形数据进行社区检测。作者提出了一个联合学习社区检测和节点表示的生成模型,这两个任务虽然高度相关,但在以往的文献中大多是独立研究的。为了实现这一点,假设每个节点可以表示为一个混合的社区,并且每个社区被定义为节点上的多项式分布。因此,利用节点和社区嵌入来生成给定节点的

论文作者

Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang,分别来自台湾国立大学、Mila-Quebec学习算法研究所,加拿大、美国哈佛大学、元素AI,加拿大、加拿大蒙特利尔高等商学院。

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Video coding, which targets to compress and reconstruct the whole frame, and feature compression, which only preserves and transmits the most critical information, stand at two ends of the scale. That is, one is with compactness and efficiency to serve for machine vision, and the other is with full fidelity, bowing to human perception. The recent endeavors in imminent trends of video compression, e.g. deep learning based coding tools and end-to-end image/video coding, and MPEG-7 compact feature descriptor standards, i.e. Compact Descriptors for Visual Search and Compact Descriptors for Video Analysis, promote the sustainable and fast development in their own directions, respectively. In this paper, thanks to booming AI technology, e.g. prediction and generation models, we carry out exploration in the new area, Video Coding for Machines (VCM), arising from the emerging MPEG standardization efforts1. Towards collaborative compression and intelligent analytics, VCM attempts to bridge the gap between feature coding for machine vision and video coding for human vision. Aligning with the rising Analyze then Compress instance Digital Retina, the definition, formulation, and paradigm of VCM are given first. Meanwhile, we systematically review state-of-the-art techniques in video compression and feature compression from the unique perspective of MPEG standardization, which provides the academic and industrial evidence to realize the collaborative compression of video and feature streams in a broad range of AI applications. Finally, we come up with potential VCM solutions, and the preliminary results have demonstrated the performance and efficiency gains. Further direction is discussed as well.

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