Title: OBI CHARiot: Full-page OBI Rubbing Segmentation with Dual Data Flywheels
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Oracle Bone Inscriptions, Data Flywheels, Instance Segmentation, Semantic Segmentation

Abstract: Oracle Bone Inscriptions (OBI), one of the earliest mature writing systems globally, serve as a crucial carrier of human civilization. However, direct segmentation of OBI characters from full-page rubbings remains underexplored, primarily due to the scarcity of high-quality annotated data.
To address this challenge, we propose a two-stage training framework named OBI CHARiot. In the first stage, a data flywheel mechanism is adopted to iteratively train the model and enable automatic pixel-level alignment. The second-stage model is enhanced through an iterative strategy that combines automated image annotation and model refinement, leveraging a large volume of additional unannotated rubbings. For reliable evaluation, we also invite experts to annotate 2,226 rubbings, resulting a high-quality test set termed OBIMDTest.
Experimental results demonstrate that OBI CHARiot offers advantages in both model performance and data quality. Specifically, models trained via OBI CHARiot outperform those trained with other frameworks, with maskAP$^{50}$ increased by \textbf{19.78\%} comparing to training directly with raw data. Additionally, several off-the-shelf instance segmentation methods trained on data processed by OBI CHARiot also exhibit better performance. Furthermore, OBI CHARiot achieves a \textbf{24.42\%} improvement in top-1 accuracy for the downstream OBI deciphering task.
These findings confirm that OBI CHARiot holds significant potential to advance the field of OBI research. To further facilitate related studies, we will release our trained model and constructed dataset, providing practical support for future investigations into Oracle Bone Inscriptions.

TL;DR: A two-stage training framework to address data misalignment and scarcity in full-page OBI rubbing segmentation sequentially