HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities
Esra Dönmez, Pascal Tilli, Hsiu-Yu Yang, Ngoc Thang Vu, Carina Silberer
Abstract
Image-Text-Matching (ITM) is one of the defacto methods of learning generalized representations from a large corpus in Vision and Language (VL). However, due to the weak association between the web-collected image–text pairs, models fail to show fine-grained understanding of the combined semantics of these modalities. To this end, we propose Hard Negative Captions (HNC): an automatically created dataset containing foiled hard negative captions for ITM training towards achieving fine-grained cross-modal comprehension in VL. Additionally, we provide a challenging manually-created test set for benchmarking models on a fine-grained cross-modal mismatch with varying levels of compositional complexity. Our results show the effectiveness of training on HNC by improving the models’ zero-shot capabilities in detecting mismatches on diagnostic tasks and performing robustly under noisy visual input scenarios. Also, we demonstrate that HNC models yield a comparable or better initialization for fine-tuning. Our code and data are publicly available.- Anthology ID:
- 2023.conll-1.24
- Volume:
- Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Jing Jiang, David Reitter, Shumin Deng
- Venue:
- CoNLL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 364–388
- Language:
- URL:
- https://aclanthology.org/2023.conll-1.24
- DOI:
- 10.18653/v1/2023.conll-1.24
- Cite (ACL):
- Esra Dönmez, Pascal Tilli, Hsiu-Yu Yang, Ngoc Thang Vu, and Carina Silberer. 2023. HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities. In Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL), pages 364–388, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities (Dönmez et al., CoNLL 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.conll-1.24.pdf