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

Fashion Style in 128 Floats

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。

For related work, see our work on parsing clothing in images and predicting fashionability.

Method

Overview of our Approach

We base our approach on combining both a classification network with a feature network that are learnt jointly with a ranking and classification loss. We do this by first defining a similarity metric on the user provided noisy tags. Using this metric we can then roughly determine semantically similar and dissimilar images. Given an anchor or reference image, we then form triplets of images by choosing a very similar and very dissimilar image to the anchor image. This allows us to define a ranking loss in which we encourage the L2 norm of the features from similar images to be small, and the L2 norm of the features from dissimilar images to be large. Although this already gives good performance, by further combining this with a small classification network and a classification loss on the dissimilar image, results can be further improved. In contrast with using features directly from classification networks, our features are optimized as an embedding using L2 norm and thus the Euclidean distance can be used directly to provide t-SNE visualizations and also similarity queries using KD-trees.

Results

t-SNE Visualization

We train our model on the Fashion144k dataset and evaluate our features both qualitatively and quantitatively on the Hipsters Wars dataset1. Above we show a visualization using t-SNE2 on the Pinup class of the Hipster Wars dataset. We can see our approach is able to group different outfits ignoring the background and the wearer. For the full visualization, click here.

For full results and details please consult the full paper.

This research was partially funded by JST CREST.

論文

2016年

Fashion Style in 128 Floats: Joint Ranking and Classification using Weak Data for Feature Extraction
Fashion Style in 128 Floats: Joint Ranking and Classification using Weak Data for Feature Extraction
Edgar Simo-Serra and Hiroshi Ishikawa
Conference in Computer Vision and Pattern Recognition (CVPR) , 2016
We propose a novel approach for learning features from weakly-supervised data by joint ranking and classification. In order to exploit data with weak labels, we jointly train a feature extraction network with a ranking loss and a classification network with a cross-entropy loss. We obtain high-quality compact discriminative features with few parameters, learned on relatively small datasets without additional annotations. This enables us to tackle tasks with specialized images not very similar to the more generic ones in existing fully-supervised datasets. We show that the resulting features in combination with a linear classifier surpass the state-of-the-art on the Hipster Wars dataset despite using features only 0.3% of the size. Our proposed features significantly outperform those obtained from networks trained on ImageNet, despite being 32 times smaller (128 single-precision floats), trained on noisy and weakly-labeled data, and using only 1.5% of the number of parameters.
@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,
}

ソフトウェア

StyleNet
StyleNet, 1.0 (2016年06月)
Fashion style in 128 floats
This code is the implementation of the "Fashion Style in 128 Floats: Joint Ranking and Classification using Weak Data for Feature Extraction". It contains the best performing feature extraction model explained in the paper.
  1. M. Hadi Kiapour, Kota Yamaguchi, Alexander C. Berg, Tamara L. Berg. Hipster Wars: Discovering Elements of Fashion Styles. In ECCV, 2014. 

  2. L.J.P. van der Maaten and G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9(Nov):2579-2605, 2008.