论文标题
2021图像相似性挑战的结果和发现
Results and findings of the 2021 Image Similarity Challenge
论文作者
论文摘要
2021图像相似性挑战引入了一个数据集，以作为评估最新图像复制检测方法的新基准。比赛中有200名参与者。本文介绍了对顶级提交的定量和定性分析。看来，最困难的图像转换涉及严重的图像作物或隐藏在无关的图像中，并结合了局部像素扰动。获胜提交中的关键算法元素是：训练强大的增强，自我监督学习，得分归一化，显式覆盖检测以及全局描述符匹配，然后进行成对图像比较。
The 2021 Image Similarity Challenge introduced a dataset to serve as a new benchmark to evaluate recent image copy detection methods. There were 200 participants to the competition. This paper presents a quantitative and qualitative analysis of the top submissions. It appears that the most difficult image transformations involve either severe image crops or hiding into unrelated images, combined with local pixel perturbations. The key algorithmic elements in the winning submissions are: training on strong augmentations, self-supervised learning, score normalization, explicit overlay detection, and global descriptor matching followed by pairwise image comparison.
