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。

  • 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

    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

    多様なファッション画像を効果的に分類できる特徴量抽出手法を提案する。 提案手法では、ランキングロスとクロスエントロピーロスを合わせて畳込みニューラルネットワークを学習させることで、 ノイズが多く含まれるようなデータセットに対しても良好に特徴抽出が行えることを示した。

  • ファッション性の推定

    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.

  • 衣服の領域分割

    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

  • Multi-Modal Embedding for Main Product Detection in Fashion
    • Multi-Modal Embedding for Main Product Detection in Fashion
    • Antonio Rubio, Longlong Yu, Edgar Simo-Serra, Francesc Moreno-Noguer
    • International Conference on Computer Vision Workshops (ICCVW) [best paper], 2017
  • Multi-Label Fashion Image Classification with Minimal Human Supervision
    • Multi-Label Fashion Image Classification with Minimal Human Supervision
    • Naoto Inoue, Edgar Simo-Serra, Toshihiko Yamasaki, Hiroshi Ishikawa
    • International Conference on Computer Vision Workshops (ICCVW), 2017
  • What Makes a Style: Experimental Analysis of Fashion Prediction
    • What Makes a Style: Experimental Analysis of Fashion Prediction
    • Moeko Takagi, Edgar Simo-Serra, Satoshi Iizuka, Hiroshi Ishikawa
    • International Conference on Computer Vision Workshops (ICCVW), 2017
  • Fashion Style in 128 Floats: Joint Ranking and Classification using Weak Data for Feature Extraction
  • Neuroaesthetics in Fashion: Modeling the Perception of Fashionability
  • A High Performance CRF Model for Clothes Parsing

ソフトウェア

  • StyleNet
  • Clothes Parsing

データセット

  • Fashion550k
    • Fashion550k
    • Large-scale weakly labelled dataset for fashion for evaluating training with noisy labels.
  • FashionStyle14
    • FashionStyle14
    • Expert-curated fashion style prediction datase with a focus on modern Japanese fashion.
  • Fashion144k (Stylenet)
    • Fashion144k (Stylenet)
    • Curated version of the large-scale weakly labelled dataset for learning fashion.
  • Fashion144k
    • Fashion144k
    • Large-scale weakly labelled dataset for predicting fashionability of fashion images.