论文标题
Google通用图像嵌入中的第一名解决方案
1st Place Solution in Google Universal Images Embedding
论文作者
论文摘要
本文介绍了Google通用图像嵌入Kaggle上的竞争的第一名解决方案。我们解决方案的突出部分是基于1）进行培训和微调的新型方法； 2）在嵌入嵌入的模型池中更好的合奏的想法； 3）在高分辨率和重叠贴片上进行微调之间的潜在权衡； 4）为动态边缘起作用的潜在因素。我们的解决方案在私人领导委员会中达到0.728，该委员会在Google Universal Images嵌入竞赛中获得第一名。
This paper presents the 1st place solution for the Google Universal Images Embedding Competition on Kaggle. The highlighted part of our solution is based on 1) A novel way to conduct training and fine-tuning; 2) The idea of a better ensemble in the pool of models that make embedding; 3) The potential trade-off between fine-tuning on high-resolution and overlapping patches; 4) The potential factors to work for the dynamic margin. Our solution reaches 0.728 in the private leader board, which achieve 1st place in Google Universal Images Embedding Competition.
