Who Are You Referring to? Coreference Resolution in Image Narrations

Abstract

Coreference resolution aims to identify words and phrases which refer to the same entity in a text, a core task in natural language processing. In this paper, we extend this task to resolving coreferences in long-form narrations of visual scenes. First, we introduce a new dataset with annotated coreference chains and their bounding boxes, as most existing image-text datasets only contain short sentences without coreferring expressions or labeled chains. We propose a new technique that learns to identify coreference chains using weak supervision, only from image-text pairs and a regularization using prior linguistic knowledge. Our model yields large performance gains over several strong baselines in resolving coreferences. We also show that coreference resolution helps improve grounding narratives in images.

Cite

Text

Goel et al. "Who Are You Referring to? Coreference Resolution in Image Narrations." International Conference on Computer Vision, 2023. doi:10.1109/ICCV51070.2023.01399

Markdown

[Goel et al. "Who Are You Referring to? Coreference Resolution in Image Narrations." International Conference on Computer Vision, 2023.](https://mlanthology.org/iccv/2023/goel2023iccv-you/) doi:10.1109/ICCV51070.2023.01399

BibTeX

@inproceedings{goel2023iccv-you,
  title     = {{Who Are You Referring to? Coreference Resolution in Image Narrations}},
  author    = {Goel, Arushi and Fernando, Basura and Keller, Frank and Bilen, Hakan},
  booktitle = {International Conference on Computer Vision},
  year      = {2023},
  pages     = {15247-15258},
  doi       = {10.1109/ICCV51070.2023.01399},
  url       = {https://mlanthology.org/iccv/2023/goel2023iccv-you/}
}