  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.
