Deep Fashion Analysis with Feature mAP Upsampling and Landmark-Driven Attention
Abstract
In this paper, we propose an attentive fashion network to address three problems of fashion analysis, namely landmark localization, category classification and attribute prediction. By utilizing a landmark prediction branch with upsampling network structure, we boost the accuracy of fashion landmark localization. With the aid of the predicted landmarks, a landmark-driven attention mechanism is proposed to help improve the precision of fashion category classification and attribute prediction. Experimental results show that our approach outperforms the state-of-the-arts on the DeepFashion dataset.
Cite
Text
Liu and Lu. "Deep Fashion Analysis with Feature mAP Upsampling and Landmark-Driven Attention." European Conference on Computer Vision Workshops, 2018. doi:10.1007/978-3-030-11015-4_4Markdown
[Liu and Lu. "Deep Fashion Analysis with Feature mAP Upsampling and Landmark-Driven Attention." European Conference on Computer Vision Workshops, 2018.](https://mlanthology.org/eccvw/2018/liu2018eccvw-deep-a/) doi:10.1007/978-3-030-11015-4_4BibTeX
@inproceedings{liu2018eccvw-deep-a,
title = {{Deep Fashion Analysis with Feature mAP Upsampling and Landmark-Driven Attention}},
author = {Liu, Jingyuan and Lu, Hong},
booktitle = {European Conference on Computer Vision Workshops},
year = {2018},
pages = {30-36},
doi = {10.1007/978-3-030-11015-4_4},
url = {https://mlanthology.org/eccvw/2018/liu2018eccvw-deep-a/}
}