# Identity-Aware Semi-Supervised Learning for Comic Character Re-Identification

arXiv: https://arxiv.org/abs/2308.09096 · HTML: https://mlsanity.com/paper/2308.09096/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 17 Aug 2023.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [A Metric Learning Reality Check](https://mlsanity.com/paper/2003.08505/) (2020)
- [Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation](https://mlsanity.com/paper/1803.11365/) (2018)
- [PyTorch Metric Learning](https://mlsanity.com/paper/2008.09164/) (2020)
- [The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives](https://mlsanity.com/paper/1611.05118/) (2016)
- [Building a Manga Dataset "Manga109" with Annotations for Multimedia Applications](https://mlsanity.com/paper/2005.04425/) (2020)
- [Unsupervised Manga Character Re-identification via Face-body and Spatial-temporal Associated Clustering](https://mlsanity.com/paper/2204.04621/) (2022)
- [CAST: Character labeling in Animation using Self-supervision by Tracking](https://mlsanity.com/paper/2201.07619/) (2022)

## Built on by (most cited first, 8 of 8)

- [The Manga Whisperer: Automatically Generating Transcriptions for Comics](https://mlsanity.com/paper/2401.10224/) (2024)
- [Tails Tell Tales: Chapter-Wide Manga Transcriptions with Character Names](https://mlsanity.com/paper/2408.00298/) (2024)
- [One missing piece in Vision and Language: A Survey on Comics Understanding](https://mlsanity.com/paper/2409.09502/) (2024)
- [From Panels to Prose: Generating Literary Narratives from Comics](https://mlsanity.com/paper/2503.23344/) (2025)
- [Character-Centric Understanding of Animated Movies](https://mlsanity.com/paper/2509.12204/) (2025)
- [Toward accessible comics for blind and low vision readers](https://mlsanity.com/paper/2407.08248/) (2024)
- [Emotion-Aware Speech Generation with Character-Specific Voices for Comics](https://mlsanity.com/paper/2509.15253/) (2025)
- [Re:Cognize -- Open-Set Comic Character Re-Identification](https://mlsanity.com/paper/2609.34032/) (2026)

## Abstract (arXiv)

Character re-identification, recognizing characters consistently across different panels in comics, presents significant challenges due to limited annotated data and complex variations in character appearances. To tackle this issue, we introduce a robust semi-supervised framework that combines metric learning with a novel 'Identity-Aware' self-supervision method by contrastive learning of face and body pairs of characters. Our approach involves processing both facial and bodily features within a unified network architecture, facilitating the extraction of identity-aligned character embeddings that capture individual identities while preserving the effectiveness of face and body features. This integrated character representation enhances feature extraction and improves character re-identification compared to re-identification by face or body independently, offering a parameter-efficient solution. By extensively validating our method using in-series and inter-series evaluation metrics, we demonstrate its effectiveness in consistently re-identifying comic characters. Compared to existing methods, our approach not only addresses the challenge of character re-identification but also serves as a foundation for downstream tasks since it can produce character embeddings without restrictions of face and body availability, enriching the comprehension of comic books. In our experiments, we leverage two newly curated datasets: the 'Comic Character Instances Dataset', comprising over a million character instances and the 'Comic Sequence Identity Dataset', containing annotations of identities within more than 3000 sets of four consecutive comic panels that we collected.