| --- |
| license: mit |
| language: |
| - en |
| pretty_name: HNC |
| size_categories: |
| - 10M<n<100M |
| --- |
| |
| # HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities |
|
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| This repository contains the dataset of the publication: HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities at CoNLL 2023 by Esra Dönmez*, Pascal Tilli*, Hsiu-Yu Yang*, Ngoc Thang Vu and Carina Silberer. |
| |
| Paper link: https://aclanthology.org/2023.conll-1.24.pdf |
| |
| |
| ## GitHub |
| Link to the official implementation: https://github.com/DigitalPhonetics/hard-negative-captions |
| |
| ## Data |
| Download the automatically generated train and validation set as well as the human-annotated test set from DaRUS: https://doi.org/10.18419/darus-4341 |
| |
| ## 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. |
| |
| ## Citation |
| ```bibtex |
| @inproceedings{hnc, |
| title = "{HNC}: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities", |
| author = {D{\"o}nmez, Esra and |
| Tilli, Pascal and |
| Yang, Hsiu-Yu and |
| Vu, Ngoc Thang and |
| Silberer, Carina}, |
| booktitle = "Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)", |
| year = "2023", |
| address = "Singapore", |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/2023.conll-1.24", |
| doi = "10.18653/v1/2023.conll-1.24", |
| pages = "364--388", |
| } |
| ``` |
| |
