C-Fashion

Dataset Introduction

C-Fashion dataset is a high-quality dataset in compositional zero-shot learning (CZSL), which is proposed in the paper "Warm-Cat: Warm-Started Test-Time Comprehensive Knowledge Accumulation for Compositional Zero-Shot Learning". C-Fashion is proposed based on the original FashionIQ, a widely used benchmark for interactive and attribute-based image retrieval. Based on the original FashionIQ images, we performed a new round of annotation using INternVL-3-8b and filtration for compositional reasoning.

Dataset Statistics

• Total Attributes: 76
• Total Objects: 28
• Total Compositions: 467
• Total Images: 29705

Uasge

Download data/C-Fashion.zip and uncompress it. Load it using dataset.py from code project of any CZSL model.

Acknowledgement

This dataset is built upon FashionIQ and relabeld by InternVL-3-8b. Thanks for their open sourse.

Citation

If you use this dataset in your research, please cite (this paper has not been released yet):

@inproceedings{WARMCAT,
  title     = {Warm-Cat: Warm-Started Test-Time Comprehensive Knowledge Accumulation for Compositional Zero-Shot Learning},
  author    = {Yan, Xudong and Feng, Songhe and Wang, Jiaxin and Su, Xin and Yi, Jin},
}