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发现相对属性的空间范围

Discovering the Spatial Extent of Relative Attributes
课程网址: http://videolectures.net/iccv2015_xiao_relative_attributes/  
主讲教师: Fanyi Xiao
开课单位: 加州大学戴维斯分校
开课时间: 2016-02-10
课程语种: 英语
中文简介:
我们提出了一种弱监督方法,该方法仅在给定成对有序图像的情况下发现相对属性的空间范围。与使用全局外观特征或依赖于关键点检测器的传统方法相比,我们的目标是自动发现与属性相关的图像区域,即使属性的外观在其属性谱中发生了巨大变化。为了实现这一点,我们首先开发了一种新的公式,该公式将检测器与局部平滑度相结合,以发现整个图像集合中的一组连贯的视觉链。然后,我们介绍了一种有效的方法来生成锚定在最初发现的链上的额外链。最后,我们自动识别最相关的视觉链,并创建一个集成图像表示来对属性进行建模。通过大量的实验,我们证明了我们的方法在建模相关属性时相对于几个基线的前景。
课程简介: We present a weakly-supervised approach that discovers the spatial extent of relative attributes, given only pairs of ordered images. In contrast to traditional approaches that use global appearance features or rely on keypoint detectors, our goal is to automatically discover the image regions that are relevant to the attribute, even when the attribute’s appearance changes drastically across its attribute spectrum. To accomplish this, we first develop a novel formulation that combines a detector with local smoothness to discover a set of coherent visual chains across the image collection. We then introduce an efficient way to generate additional chains anchored on the initial discovered ones. Finally, we automatically identify the most relevant visual chains, and create an ensemble image representation to model the attribute. Through extensive experiments, we demonstrate our method’s promise relative to several baselines in modeling relative attributes.
关 键 词: 相对属性; 空间范围; 监督方法
课程来源: 视频讲座网
数据采集: 2023-06-19:chenxin01
最后编审: 2023-06-19:chenxin01
阅读次数: 18