Wei Shi

In this paper, we design a Position Constraint Loss (PCLoss) for fashion landmark estimation, which incorporates the position correlation into landmark estimation models. Specifically, the PCLoss adds a regular term for each landmark to regularize their relative positions. Compared with other alternatives, our PCLoss effectively mitigates the outliers and duplicate detection problems without modifying existing CNN architectures. In addition, our skeleton-like optimization method further strengthens the position constraints between landmarks. The proposed method can be applied to both regression and heatmap based methods and it provides a novel perspective towards position relation learning in key point estimation tasks. Extensive experimental results on three challenging datasets, DeepFashion, FLD and FashionAI, demonstrate that our method outperforms other state-of-the-art methods. The experiment on COCO 2017 shows the potential applications of PCLoss for other key point estimation tasks, which can be explored more in future work.

Oct 1, 2021

A Position Constraint Loss (PCLoss) method for fashion landmark estimation that constrains error landmark locations by utilizing position relationships, applicable to both regression and heatmap-based methods without modifying network structure.

May 4, 2020

A Self-Refining Deep Symmetry Enhanced Network (DSEN) for rain removal that extracts rotation equivariant features and uses a self-refining mechanism to remove accumulated rain streaks in a coarse-to-fine manner.

Sep 22, 2019