@inproceedings{bhattacharyya-etal-2024-recap,
    title = "{R}e{CAP}: Semantic Role Enhanced Caption Generation",
    author = "Bhattacharyya, Abhidip  and
      Palmer, Martha  and
      Heckman, Christoffer",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1191",
    pages = "13633--13649",
    abstract = "Even though current vision language (V+L) models have achieved success in generating image captions, they often lack specificity and overlook various aspects of the image. Additionally, the attention learned through weak supervision operates opaquely and is difficult to control. To address these limitations, we propose the use of semantic roles as control signals in caption generation. Our hypothesis is that, by incorporating semantic roles as signals, the generated captions can be guided to follow specific predicate argument structures. To validate the effectiveness of our approach, we conducted experiments using data and compared the results with a baseline model VL-BART(CITATION). The experiments showed a significant improvement, with a gain of 45{\%} in Smatch score (Standard NLP evaluation metric for semantic representations), demonstrating the efficacy of our approach. By focusing on specific objects and their associated semantic roles instead of providing a general description, our framework produces captions that exhibit enhanced quality, diversity, and controllability.",
}

Even though current vision language (V+L) models have achieved success in generating image captions, they often lack specificity and overlook various aspects of the image. Additionally, the attention learned through weak supervision operates opaquely and is difficult to control. To address these limitations, we propose the use of semantic roles as control signals in caption generation. Our hypothesis is that, by incorporating semantic roles as signals, the generated captions can be guided to follow specific predicate argument structures. To validate the effectiveness of our approach, we conducted experiments using data and compared the results with a baseline model VL-BART(CITATION). The experiments showed a significant improvement, with a gain of 45{\%} in Smatch score (Standard NLP evaluation metric for semantic representations), demonstrating the efficacy of our approach. By focusing on specific objects and their associated semantic roles instead of providing a general description, our framework produces captions that exhibit enhanced quality, diversity, and controllability.
Bhattacharyya, Abhidip Palmer, Martha Heckman, Christoffer
ReCAP: Semantic Role Enhanced Caption Generation