论文标题
支持大规模的图像识别范围样品
Supporting large-scale image recognition with out-of-domain samples
论文作者
论文摘要
本文提出了一种有效的端到端方法，用于执行用于标记和排名地标图像的任务的实例级别识别。第一步，我们使用卷积神经网络将图像嵌入了高维特征空间中，该卷积神经网络训练有添加角度余量损失，并使用视觉相似性对图像进行了分类。然后，我们有效地重新排列了预测，并利用与室外图像的相似性过滤噪声。使用这种方法，我们获得了2020年Google Landmark识别挑战赛的第一名。
This article presents an efficient end-to-end method to perform instance-level recognition employed to the task of labeling and ranking landmark images. In a first step, we embed images in a high dimensional feature space using convolutional neural networks trained with an additive angular margin loss and classify images using visual similarity. We then efficiently re-rank predictions and filter noise utilizing similarity to out-of-domain images. Using this approach we achieved the 1st place in the 2020 edition of the Google Landmark Recognition challenge.
