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    <titleInfo>
        <title>Data-Efficient Language Shaped Few-shot Image Classification</title>
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        <namePart type="given">Zhenwen</namePart>
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        <namePart type="given">Xiangliang</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2021</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <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 ShapeWorld and 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).</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.400</identifier>
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        <date>2021-11</date>
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            <start>4680</start>
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%0 Conference Proceedings
%T Data-Efficient Language Shaped Few-shot Image Classification
%A Liang, Zhenwen
%A Zhang, Xiangliang
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F liang-zhang-2021-data-efficient
%X 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 ShapeWorld and 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).
%R 10.18653/v1/2021.findings-emnlp.400
%U https://aclanthology.org/2021.findings-emnlp.400
%U https://doi.org/10.18653/v1/2021.findings-emnlp.400
%P 4680-4686

Markdown (Informal)

[Data-Efficient Language Shaped Few-shot Image Classification](https://aclanthology.org/2021.findings-emnlp.400) (Liang & Zhang, Findings 2021)

• Data-Efficient Language Shaped Few-shot Image Classification (Liang & Zhang, Findings 2021)

ACL

• Zhenwen Liang and Xiangliang Zhang. 2021. Data-Efficient Language Shaped Few-shot Image Classification. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4680–4686, Punta Cana, Dominican Republic. Association for Computational Linguistics.