# Pose Guided Attention for Multi-label Fashion Image Classification

arXiv: https://arxiv.org/abs/1911.05024 · HTML: https://mlsanity.com/paper/1911.05024/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [Human Shape and Clothing Estimation](https://mlsanity.com/paper/2402.18032/) (2024)

## Abstract (arXiv)

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 par 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.