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Region Proposal Based Semantic Matcher

机译:基于地区的语义匹配

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摘要

Unseen object detection problem is known as a semantic matching problem. Thus, a semantic matcher takes two images as an input - the request image and the test image. The request image represents an object class needed to be found on the test image. In this paper, we propose a new region proposal based semantic matcher. In our region based semantic matcher we use the same ideas as in R-CNN. Our Body CNN also generates proposals similar to classical Faster R-CNN, and Head-CNN compares proposals with a request descriptor, extracted from the request image. To extract features from the request image we use Request descriptor CNN. All three CNNs - Head, Body and Request descriptor are trained together, end-to-end for seen class object detection by request and then applied to both seen and unseen classes. We have trained and tested our CNN on Pascal VOC Dataset.
机译:未知眼图的对象检测问题被称为语义匹配问题。因此,语义匹配器将两个图像作为输入拍摄 - 请求图像和测试图像。请求图像表示在测试图像上找到所需的对象类。在本文中,我们提出了一个新的地区基于建议的语义拼贴。在我们的地区的语义匹配中,我们使用与R-CNN中相同的想法。我们的身体CNN还生成类似于古典R-CNN的提案,并且HEAD-CNN将从请求描述符与请求图像提取的提案进行比较。要从请求图像中提取要素,我们使用请求描述符CNN。所有三个CNNS - 头部,正文和请求描述符都培训,以便通过请求进行端到端用于查找类对象检测,然后应用于视野和看不见的类。我们在Pascal VOC数据集上培训并测试了我们的CNN。

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