论文标题
对原始和伪标签对汽车定位问题弱监督学习的影响的比较研究
A Comparative Study on Effects of Original and Pseudo Labels for Weakly Supervised Learning for Car Localization Problem
论文作者
论文摘要
在这项研究中，由于多种概念含义对使用CAR数据集上呈现弱监督的学习对本地化而产生的不同类标签的影响。另外，生成的标签包括在比较中，解决方案变成了无监督的学习。本文使用其他方法调查了在图像中定位的多个设置，而不是监督学习。为了预测定位标签，可以实现类激活映射（CAM），从结果中，通过使用形态边缘检测提取边界框。除了原始的类标签外，生成的类标签还用于训练凸轮，在该标签上转向解决方案，以进行无监督的学习示例。在实验中，我们首先分析了类标签在薄弱监督数据集上的弱监督本地化中的影响。然后，我们表明，所提出的无监督方法的表现使该特定数据集中的弱监督方法大约比％6。
In this study, the effects of different class labels created as a result of multiple conceptual meanings on localization using Weakly Supervised Learning presented on Car Dataset. In addition, the generated labels are included in the comparison, and the solution turned into Unsupervised Learning. This paper investigates multiple setups for car localization in the images with other approaches rather than Supervised Learning. To predict localization labels, Class Activation Mapping (CAM) is implemented and from the results, the bounding boxes are extracted by using morphological edge detection. Besides the original class labels, generated class labels also employed to train CAM on which turn to a solution to Unsupervised Learning example. In the experiments, we first analyze the effects of class labels in Weakly Supervised localization on the Compcars dataset. We then show that the proposed Unsupervised approach outperforms the Weakly Supervised method in this particular dataset by approximately %6.
