Aggregation and Finetuning for Clothes Landmark Detection
This addresses a fundamental problem in computer vision for applications like fashion analysis, though it appears incremental as it builds on existing detection frameworks.
The paper tackles clothes landmark detection by proposing a new training scheme called Aggregation and Finetuning, which leverages homogeneity among landmarks across clothing categories. It achieves state-of-the-art results, winning first place in the DeepFashion2 Challenge 2020 with an AP of 0.590 on the test set and 0.615 on the validation set.
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 .