论文标题
汇总和固定衣服的地标检测
Aggregation and Finetuning for Clothes Landmark Detection
论文作者
论文摘要
对于许多应用来说，衣服的具有里程碑意义的检测是一个基本问题。在本文中，提出了针对衣服地标检测的新培训计划：提出了$ \ textit {聚合和填充} $。我们研究了不同类别衣服的地标之间的同质性，并利用它来设计培训程序。广泛的实验表明，我们的方法的表现优于当前最新方法。我们的方法还赢得了2020年DeepFashion2 Challenge的第一名 - 衣服地标估计轨道，测试集的AP为0.590，在验证集中为0.615。代码将在https://github.com/lzhbrian/deepfashion2-kps-agg-finetune上公开获取。
Landmark detection for clothes is a fundamental problem for many applications. In this paper, a new training scheme for clothes landmark detection: $\textit{Aggregation and Finetuning}$, is proposed. We investigate the homogeneity among landmarks of different categories of clothes, and utilize it to design the procedure of training. Extensive experiments show that our method outperforms current state-of-the-art methods by a large margin. Our method also won the 1st place in the DeepFashion2 Challenge 2020 - Clothes Landmark Estimation Track with an AP of 0.590 on the test set, and 0.615 on the validation set. Code will be publicly available at https://github.com/lzhbrian/deepfashion2-kps-agg-finetune .
