论文标题
基于图像的车辆重新识别模型，具有自适应注意模块和元数据重新排列
Image-based Vehicle Re-identification Model with Adaptive Attention Modules and Metadata Re-ranking
论文作者
论文摘要
车辆重新识别是一项具有挑战性的任务，这是由于类内的可变性和在非重叠摄像机之间的类间相似性。为了解决这些问题，最近提出的方法需要附加注释，以提取更多特征，以供假阳性图像排除。在本文中，我们提出了一个由自适应注意模块提供动力的模型，该模型需要更少的标签注释，但仍超过先前的模型。我们还包括一种重新级别方法，该方法考虑了论文中元数据功能嵌入的重要性。提出的方法在CVPR AI City Challenge 2020数据集上进行了评估，并在轨道2中获得了37.25％的地图。
Vehicle Re-identification is a challenging task due to intra-class variability and inter-class similarity across non-overlapping cameras. To tackle these problems, recently proposed methods require additional annotation to extract more features for false positive image exclusion. In this paper, we propose a model powered by adaptive attention modules that requires fewer label annotations but still out-performs the previous models. We also include a re-ranking method that takes account of the importance of metadata feature embeddings in our paper. The proposed method is evaluated on CVPR AI City Challenge 2020 dataset and achieves mAP of 37.25% in Track 2.
