MetaCloth: Learning Unseen Tasks of Dense Fashion Landmark Detection From a Few Samples

@article{Ge2022MetaClothLU,
  title={MetaCloth: Learning Unseen Tasks of Dense Fashion Landmark Detection From a Few Samples},
  author={Yuying Ge and Ruimao Zhang and Ping Luo},
  journal={IEEE Transactions on Image Processing},
  year={2022},
  volume={31},
  pages={1120-1133}
}
Recent advanced methods for fashion landmark detection are mainly driven by training convolutional neural networks on large-scale fashion datasets, which has a large number of annotated landmarks. However, such large-scale annotations are difficult and expensive to obtain in real-world applications, thus models that can generalize well from a small amount of labelled data are desired. We investigate this problem of few-shot fashion landmark detection, where only a few labelled samples are… 
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