# Pose Guided Attention for Multi-label Fashion Image Classification

Source: [Pose Guided Attention for Multi-label Fashion Image Classification](<https://www.sota2.com/research/paper/68fa66ae1e209131cea11b54>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:56:21.324Z

Authors: Beatriz Quintino Ferreira, Jo\\~ao P. Costeira, Ricardo G. Sousa, Liang-Yan Gui, Jo\\~ao P. Gomes

Published: 2019-11-12T17:32:53.000Z

## 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 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1911.05024>)

[PDF](<https://arxiv.org/pdf/1911.05024.pdf>)