论文标题
大规模检索的两阶段判别重新排列
Two-stage Discriminative Re-ranking for Large-scale Landmark Retrieval
论文作者
论文摘要
我们为大规模地标图像检索提出了有效的管道，该管道通过两阶段的判别重新排列来解决数据集的多样性。我们的方法是基于使用综合神经网络将图像嵌入功能空间中的，该网络训练有余弦软效果。由于图像的差异，其中包括极端的观点变化，例如必须从内部图像中检索地标的外部图像，因此对于仅基于视觉相似性的方法而言，这对于方法非常具有挑战性。我们提出的重新排列方法以两个步骤改进结果：在排序中，$ k $ - 最近的邻居搜索具有软投票，以根据其标签与查询图像的标签相似性对检索结果进行排序，在插入步骤中，我们添加了数据集中未通过图像模拟率检索的数据集中的其他样本。这种方法允许在检索图像中克服低视觉多样性。深入的实验结果表明，所提出的方法在具有挑战性的Google Landmarks数据集上大大优于现有方法。使用我们的方法，我们在Google Landmark 2019挑战赛中获得了第一名，在Kaggle上的Google Landmark识别2019挑战中获得了第三名。我们的代码在此处公开可用：\ url {https://github.com/lyakaap/landmark2019-1st-and-3rd-place-solution}
We propose an efficient pipeline for large-scale landmark image retrieval that addresses the diversity of the dataset through two-stage discriminative re-ranking. Our approach is based on embedding the images in a feature-space using a convolutional neural network trained with a cosine softmax loss. Due to the variance of the images, which include extreme viewpoint changes such as having to retrieve images of the exterior of a landmark from images of the interior, this is very challenging for approaches based exclusively on visual similarity. Our proposed re-ranking approach improves the results in two steps: in the sort-step, $k$-nearest neighbor search with soft-voting to sort the retrieved results based on their label similarity to the query images, and in the insert-step, we add additional samples from the dataset that were not retrieved by image-similarity. This approach allows overcoming the low visual diversity in retrieved images. In-depth experimental results show that the proposed approach significantly outperforms existing approaches on the challenging Google Landmarks Datasets. Using our methods, we achieved 1st place in the Google Landmark Retrieval 2019 challenge and 3rd place in the Google Landmark Recognition 2019 challenge on Kaggle. Our code is publicly available here: \url{https://github.com/lyakaap/Landmark2019-1st-and-3rd-Place-Solution}
