ScaleNet：在超市及其他地方指导目标提案生成
ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond
摘要：
本文以超市商品检测为背景，研究了超市图像和其他自然图像中的目标建议生成问题。我们认为，图像中的对象尺度的估计是有帮助的生成对象的建议，特别是对于超市的图像，对象尺度通常是在一个小的范围内。因此，我们建议在生成对象建议之前估计图像的对象尺度。所提出的预测对象尺度的方法称为ScaleNet。为了验证ScaleNet的有效性，我们构建了三个超市数据集，其中两个是用于测试的真实数据集，另一个是用于训练的合成数据集。简而言之，我们通过添加规模预测阶段来扩展先前最先进的对象建议方法。由此产生的方法优于以前的国家的最先进的超市数据集上的一个很大的利润。我们还表明，该方法适用于其他自然图像上的对象建议，并且在MS COCO数据集上优于以前最先进的对象建议方法。超市数据集、虚拟超市以及创建更多合成数据集的工具将被公开。
英文摘要：
Motivated by product detection in supermarkets, this paper studies the problem of object proposal generation in supermarket images and other natural images. We argue that estimation of object scales in images is helpful for generating object proposals, especially for supermarket images where object scales are usually within a small range. Therefore, we propose to estimate object scales of images before generating object proposals. The proposed method for predicting object scales is called ScaleNet. To validate the effectiveness of ScaleNet, we build three supermarket datasets, two of which are real-world datasets used for testing and the other one is a synthetic dataset used for training. In short, we extend the previous state-of-the-art object proposal methods by adding a scale prediction phase. The resulted method outperforms the previous state-of-the-art on the supermarket datasets by a large margin. We also show that the approach works for object proposal on other natural images and it outperforms the previous state-of-the-art object proposal methods on the MS COCO dataset. The supermarket datasets, the virtual supermarkets, and the tools for creating more synthetic datasets will be made public.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
