Pose Guided Attention for Multi-Label Fashion Image Classification

Beatriz Quintino Ferreira, João Paulo Costeira, Ricardo Gamelas Sousa, Liang-Yan Gui, João Pedro Gomes

ICCVW 2019 pp. 3125-3128

doi:10.1109/ICCVW.2019.00380 /iccvw/2019/ferreira2019iccvw-pose/

Abstract

We propose a compact framework with guided attention for multi-label classification in the fashion domain. Our visual semantic attention model (VSAM) is supervised by automatic pose extraction creating a discriminative feature space. VSAM outperforms the state of the art for an in-house dataset and performs on pair with previous works on the DeepFashion dataset, even without using any landmark annotations. Additionally, we show that our semantic attention module brings robustness to large quantities of wrong annotations and provides more interpretable results.

PDF ICCVW Semantic Scholar

Cite

Text

Ferreira et al. "Pose Guided Attention for Multi-Label Fashion Image Classification." IEEE/CVF International Conference on Computer Vision Workshops, 2019. doi:10.1109/ICCVW.2019.00380

Markdown

[Ferreira et al. "Pose Guided Attention for Multi-Label Fashion Image Classification." IEEE/CVF International Conference on Computer Vision Workshops, 2019.](https://mlanthology.org/iccvw/2019/ferreira2019iccvw-pose/) doi:10.1109/ICCVW.2019.00380

BibTeX

@inproceedings{ferreira2019iccvw-pose,
  title     = {{Pose Guided Attention for Multi-Label Fashion Image Classification}},
  author    = {Ferreira, Beatriz Quintino and Costeira, João Paulo and Sousa, Ricardo Gamelas and Gui, Liang-Yan and Gomes, João Pedro},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2019},
  pages     = {3125-3128},
  doi       = {10.1109/ICCVW.2019.00380},
  url       = {https://mlanthology.org/iccvw/2019/ferreira2019iccvw-pose/}
}