Xin Liu
ABSTRACT
We present MMFashion, a comprehensive, flexible and user-friendly open-source visual fashion analysis toolbox based on PyTorch. This toolbox supports a wide spectrum of fashion analysis tasks, including Fashion Attribute Prediction, Fashion Recognition and Retrieval, Fashion Landmark Detection, Fashion Parsing and Segmentation and Fashion Compatibility and Recommendation. It covers almost all the mainstream tasks in fashion analysis community. MMFashion has several appealing properties. Firstly, MMFashion follows the principle of modular design. The framework is decomposed into different components so that it is easily extensible for diverse customized modules. In addition, detailed documentations, demo scripts and off-the-shelf models are available, which ease the burden of layman users to leverage the recent advances in deep learning-based fashion analysis. Our proposed MMFashion is currently the most complete platform for visual fashion analysis in deep learning era, with more functionalities to be added. This toolbox and the benchmark could serve the flourishing research community by providing a flexible toolkit to deploy existing models and develop new ideas and approaches. We welcome all contributions to this still-growing efforts towards open science: https://github.com/open-mmlab/mmfashion.
- 2018. deep-fashion-retrieval. https://github.com/ihciah/deep-fashion-retrieval.Google Scholar
- 2018. tf.fashionAI. https://github.com/HiKapok/tf.fashionAI.Google Scholar
- 2019. fashion-recommendation. https://github.com/khanhnamle1994/fashion- recommendationGoogle Scholar
- Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, et almbox. 2019. MMDetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155 (2019).Google Scholar
- Yunchao Gong, Yangqing Jia, Thomas Leung, Alexander Toshev, and Sergey Ioffe. 2013. Deep convolutional ranking for multilabel image annotation. arXiv preprint arXiv:1312.4894 (2013).Google Scholar
- Xintong Han, Zuxuan Wu, Yu-Gang Jiang, and Larry S Davis. 2017. Learning fashion compatibility with bidirectional lstms. In ACM MM. Google ScholarDigital Library
- Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. 2017. Mask r-cnn. In ICCV.Google Scholar
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR.Google Scholar
- Xin Liu, Yongbin Sun, Ziwei Liu, and Dahua Lin. 2020. Learning Diverse Fashion Collocation by Neural Graph Filtering. arXiv preprint arXiv:2003.04888 (2020).Google Scholar
- Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaoou Tang. 2016a. Deepfashion: Powering robust clothes recognition and retrieval with rich annotations. In CVPR.Google Scholar
- Ziwei Liu, Sijie Yan, Ping Luo, Xiaogang Wang, and Xiaoou Tang. 2016b. Fashion landmark detection in the wild. In ECCV.Google Scholar
- Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017. Automatic differentiation in pytorch. (2017).Google Scholar
- Wu Shi, Tak-Wai Hui, Ziwei Liu, Dahua Lin, and Chen Change Loy. 2019. Learning to Synthesize Fashion Textures. arXiv preprint arXiv:1911.07472 (2019).Google Scholar
- Mariya I Vasileva, Bryan A Plummer, Krishna Dusad, Shreya Rajpal, Ranjitha Kumar, and David Forsyth. 2018. Learning type-aware embeddings for fashion compatibility. In ECCV.Google Scholar
- Han Xiao, Kashif Rasul, and Roland Vollgraf. 2017. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. [arXiv]cs.LG/1708.07747 [cs.LG]Google Scholar
- Sijie Yan, Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaoou Tang. 2017. Unconstrained fashion landmark detection via hierarchical recurrent transformer networks. In ACM MM. Google ScholarDigital Library
Index Terms
- MMFashion: An Open-Source Toolbox for Visual Fashion Analysis
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