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Manga109Dialog: A Large-scale Dialogue Dataset for Comics Speaker Detection

Official repository of Manga109Dialog (ICME 2024) | Paper | Dataset

Overview

To enhance the machine’s understanding of comics, we developed Manga109Dialog, which is the world’s largest speaker-to-text annotation dataset for comics. We proposed a novel deep learning-based method using scene graph generation (SGG) models, providing a challenging yet realistic benchmark for comics speaker detection. The contributions of our work are summarized as follows.

• We constructed Manga109Dialog, an annotation dataset of associations between speakers and texts. This is the largest comics dialogue dataset in the world.
• We proposed a deep learning-based approach for comics speaker detection using SGG models. We enhance the performance by introducing frame information in the relationship prediction stage.
• We established a new benchmark for evaluation, setting a standard for future research in this domain.

Prerequisites

• Manga109 dataset
  • Download from http://www.manga109.org/en/download.html
• Manga109Dialog annotation
  • Download from https://github.com/manga109/public-annotations

Environment setup

Check INSTALL.md for installation instructions.

Data preprocessing

Convert the annotations from Manga109 into a format suitable for the scene graph generation (SGG) models. For more details, check README.md.

Speaker prediction

This is the core part of our model. For details on how to detect characters and texts in comics and predict the speaker based on visual information, check README.md.

Evaluation

In addition to conventional metrics for evaluating SGG models, we have introduced a new metric tailored for comics: Recall@(#text).

# PredCls / SGCls
python eval_and_vis/eval_original.py

# SGDet
python eval_and_vis/eval_original_sgdet.py

You can find details on conventional evaluation metrics in METRICS.md.

Visualization

The visualization tools for predictions can be found in eval_and_vis/.

• 1.visualize_PredCls_and_SGCls.ipynb
• 2.visualize_SGDet.ipynb
• 3.visualize_SGDet.ipynb
• 4.visualize_custom_SGDet.ipynb

Citation

When using annotations of Manga109Dialog, please cite our paper.

@inproceedings{li2024manga109dialog,
  title={Manga109Dialog: A Large-scale Dialogue Dataset for Comics Speaker Detection},
  author={Li, Yingxuan and Aizawa, Kiyoharu and Matsui, Yusuke},
  booktitle={Proceedings of the IEEE International Conference on Multimedia and Expo},
  year={2024}
}