ファッションの研究
ファッションは日常生活で重要な役割を果たしており、言葉を使わずに多くの情報を伝えている。しかし、ファッションは非常に主観的なテーマで、データにラベルを付けることは至難の業である。本研究は、ネットにある大規模な弱教師なしデータを利用し、ファッシ>ョンの理解を目指す。
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ランキングロスと分類ロスにもとづくファッションデータの特徴抽出
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ファッション性の推定
Being able to understand and model fashion can have a great impact in everyday life. From choosing your outfit in the morning to picking your best picture for your social network profile, we make fashion decisions on a daily basis that can have impact on our lives. As not everyone has access to a fashion expert to give advice on the current trends and what picture looks best, we have been working on developing systems that are able to automatically learn about fashion and provide useful recommendations to users. In this work we focus on building models that are able to discover and understand fashion. For this purpose we have created the Fashion144k dataset, consisting of 144,169 user posts with images and their associated metadata. We exploit the votes given to each post by different users to obtain measure of fashionability, that is, how fashionable the user and their outfit is in the image. We propose the challenging task of identifying the fashionability of the posts and present an approach that by combining many different sources of information, is not only able to predict fashionability, but it is also able to give fashion advice to the users.
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衣服の領域分割
In this research we focus on the semantic segmentation of clothings from still images. This is a very complex task due to the large number of classes where intra-class variability can be larger than inter-class variability. We propose a Conditional Random Field (CRF) model that is able to leverage many different image features to obtain state-of-the-art performance on the challenging Fashionista dataset.
論文
@InProceedings{KikuchiICCVW2019, author = {Kotaro Kikuchi and Kota Yamaguchi and Edgar Simo-Serra and Tetsunori Kobayashi}, title = {{Regularized Adversarial Training for Single-shot Virtual Try-On}}, booktitle = "Proceedings of the International Conference on Computer Vision Workshops (ICCVW)", year = 2019, }
@InProceedings{TakagiICCVW2017, author = {Moeko Takagi and Edgar Simo-Serra and Satoshi Iizuka and Hiroshi Ishikawa}, title = {{What Makes a Style: Experimental Analysis of Fashion Prediction}}, booktitle = "Proceedings of the International Conference on Computer Vision Workshops (ICCVW)", year = 2017, }
@InProceedings{RubioICCVW2017, author = {Antonio Rubio and Longlong Yu and Edgar Simo-Serra and Francesc Moreno-Noguer}, title = {{Multi-Modal Embedding for Main Product Detection in Fashion}}, booktitle = "Proceedings of the International Conference on Computer Vision Workshops (ICCVW)", year = 2017, }
@InProceedings{InoueICCVW2017, author = {Naoto Inoue and Edgar Simo-Serra and Toshihiko Yamasaki and Hiroshi Ishikawa}, title = {{Multi-Label Fashion Image Classification with Minimal Human Supervision}}, booktitle = "Proceedings of the International Conference on Computer Vision Workshops (ICCVW)", year = 2017, }
@InProceedings{SimoSerraCVPR2016, author = {Edgar Simo-Serra and Hiroshi Ishikawa}, title = {{Fashion Style in 128 Floats: Joint Ranking and Classification using Weak Data for Feature Extraction}}, booktitle = "Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR)", year = 2016, }
@InProceedings{SimoSerraCVPR2015, author = {Edgar Simo-Serra and Sanja Fidler and Francesc Moreno-Noguer and Raquel Urtasun}, title = {{Neuroaesthetics in Fashion: Modeling the Perception of Fashionability}}, booktitle = "Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR)", year = 2015, }
@InProceedings{SimoSerraACCV2014, author = {Edgar Simo-Serra and Sanja Fidler and Francesc Moreno-Noguer and Raquel Urtasun}, title = {{A High Performance CRF Model for Clothes Parsing}}, booktitle = "Proceedings of the Asian Conference on Computer Vision (ACCV)", year = 2014, }