Retrieval, Analogy, and Composition: A framework for Compositional Generalization in Image Captioning
Zhan Shi, Hui Liu, Martin Renqiang Min, Christopher Malon, Li Erran Li, Xiaodan Zhu
Abstract
Image captioning systems are expected to have the ability to combine individual concepts when describing scenes with concept combinations that are not observed during training. In spite of significant progress in image captioning with the help of the autoregressive generation framework, current approaches fail to generalize well to novel concept combinations. We propose a new framework that revolves around probing several similar image caption training instances (retrieval), performing analogical reasoning over relevant entities in retrieved prototypes (analogy), and enhancing the generation process with reasoning outcomes (composition). Our method augments the generation model by referring to the neighboring instances in the training set to produce novel concept combinations in generated captions. We perform experiments on the widely used image captioning benchmarks. The proposed models achieve substantial improvement over the compared baselines on both composition-related evaluation metrics and conventional image captioning metrics.- Anthology ID:
- 2021.findings-emnlp.171
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1990–2000
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.171
- DOI:
- 10.18653/v1/2021.findings-emnlp.171
- Cite (ACL):
- Zhan Shi, Hui Liu, Martin Renqiang Min, Christopher Malon, Li Erran Li, and Xiaodan Zhu. 2021. Retrieval, Analogy, and Composition: A framework for Compositional Generalization in Image Captioning. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1990–2000, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Retrieval, Analogy, and Composition: A framework for Compositional Generalization in Image Captioning (Shi et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2021.findings-emnlp.171.pdf
- Data
- MS COCO