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 -> 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.- Anthology ID:
- 2021.eacl-main.196
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2315–2320
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.196
- DOI:
- 10.18653/v1/2021.eacl-main.196
- Cite (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.
- Cite (Informal):
- L2C: Describing Visual Differences Needs Semantic Understanding of Individuals (Yan et al., EACL 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.eacl-main.196.pdf
- Data
- CUB-200-2011