A Multi-modal Corpus of Fashion Images and Descriptions in German

The FEIDEGGER (fashion images and descriptions in German) dataset is a new multi-modal corpus that focuses specifically on the domain of fashion items and their visual descriptions in German. The dataset was created as part of ongoing research at Zalando into text-image multi-modality in the area of fashion.

The dataset itself consists of 8732 high-resolution images, each depicting a dress from the available on the Zalando shop against a white-background. For each of the images we provide five textual annotations in German, each of which has been generated by a separate user. The example above shows 2 of the 5 descriptions for a dress (English translations only given for illustration, but not part of the dataset).

Unlike other tasks typically encountered in multi-modal learning, in fashion the informative information in the visual data often consists of very fine-grained details that needs to be reflected in the textual descriptions. Furthermore in order to generate such detailed descriptions, users must often rely on a domain-specific vocabulary. These particularities make the creation of a multi-modal fashion-related dataset a challenging task.

In order to create FEIDEGGER we leveraged crowd-sourcing while developing a novel annotation and assessment pipeline in order to ensure the high-quality of the final dataset. The pipeline and motivation behind various design decisions can be found in our published work.

For more information on the dataset, please check out LREC 2018 paper.

Please cite the following paper when using FEIDEGGER:

@inproceedings{lefakis2018feidegger,
  title={FEIDEGGER: A Multi-modal Corpus of Fashion Images and Descriptions in German},
  author={Lefakis, Leonidas and Akbik, Alan and Vollgraf, Roland},
  booktitle = {{LREC} 2018, 11th Language Resources and Evaluation Conference},
  year      = {2018}
}