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        <title>L2C: Describing Visual Differences Needs Semantic Understanding of Individuals</title>
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    <abstract>Recent advances in language and vision push forward the research of captioning a single image to describing visual differences between image pairs. Suppose there are two images, I_1 and I_2, and the task is to generate a description W_1,2 comparing them, existing methods directly model I_1, I_2 -\textgreater W_1,2 mapping without the semantic understanding of individuals. In this paper, we introduce a Learning-to-Compare (L2C) model, which learns to understand the semantic structures of these two images and compare them while learning to describe each one. We demonstrate that L2C benefits from a comparison between explicit semantic representations and single-image captions, and generalizes better on the new testing image pairs. It outperforms the baseline on both automatic evaluation and human evaluation for the Birds-to-Words dataset.</abstract>
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%0 Conference Proceedings
%T L2C: Describing Visual Differences Needs Semantic Understanding of Individuals
%A Yan, An
%A Wang, Xin
%A Fu, Tsu-Jui
%A Wang, William Yang
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F yan-etal-2021-l2c
%X Recent advances in language and vision push forward the research of captioning a single image to describing visual differences between image pairs. Suppose there are two images, I_1 and I_2, and the task is to generate a description W_1,2 comparing them, existing methods directly model I_1, I_2 -\textgreater W_1,2 mapping without the semantic understanding of individuals. In this paper, we introduce a Learning-to-Compare (L2C) model, which learns to understand the semantic structures of these two images and compare them while learning to describe each one. We demonstrate that L2C benefits from a comparison between explicit semantic representations and single-image captions, and generalizes better on the new testing image pairs. It outperforms the baseline on both automatic evaluation and human evaluation for the Birds-to-Words dataset.
%R 10.18653/v1/2021.eacl-main.196
%U https://aclanthology.org/2021.eacl-main.196
%U https://doi.org/10.18653/v1/2021.eacl-main.196
%P 2315-2320

Markdown (Informal)

[L2C: Describing Visual Differences Needs Semantic Understanding of Individuals](https://aclanthology.org/2021.eacl-main.196) (Yan et al., EACL 2021)

• L2C: Describing Visual Differences Needs Semantic Understanding of Individuals (Yan et al., EACL 2021)

ACL

• An Yan, Xin Wang, Tsu-Jui Fu, and William Yang Wang. 2021. L2C: Describing Visual Differences Needs Semantic Understanding of Individuals. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2315–2320, Online. Association for Computational Linguistics.