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).- Anthology ID:
- 2021.findings-emnlp.400
- 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:
- 4680–4686
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.400
- DOI:
- 10.18653/v1/2021.findings-emnlp.400
- Cite (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.
- Cite (Informal):
- Data-Efficient Language Shaped Few-shot Image Classification (Liang & Zhang, Findings 2021)
- PDF:
- https://preview.aclanthology.org/landing_page/2021.findings-emnlp.400.pdf
- Code
- derderking/df-lsl
- Data
- CUB-200-2011, ShapeWorld