@inproceedings{liang-zhang-2021-data-efficient,
    title = "Data-Efficient Language Shaped Few-shot Image Classification",
    author = "Liang, Zhenwen  and
      Zhang, Xiangliang",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.findings-emnlp.400/",
    doi = "10.18653/v1/2021.findings-emnlp.400",
    pages = "4680--4686",
    abstract = "Many existing works have demonstrated that language is a helpful guider for image understanding by neural networks. We focus on a language-shaped learning problem in a few-shot setting, i.e., using language to improve few-shot image classification when language descriptions are only available during training. We propose a data-efficient method that can make the best usage of the few-shot images and the language available only in training. Experimental results on dataset \textit{ShapeWorld} and \textit{Birds} show that our method outperforms other state-of-the-art baselines in language-shaped few-shot learning area, especially when training data is more severely limited. Therefore, we call our approach data-efficient language-shaped learning (DF-LSL)."
}