Aggregation and Finetuning for Clothes Landmark Detection

05/01/2020

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by   Tzu-heng Lin, et al.

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Landmark detection for clothes is a fundamental problem for many applications. In this paper, a new training scheme for clothes landmark detection: 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 .