Guided by Principles of Composition: A Domain‐Specific Priors Based Detector for Recognizing Ritual Implements in Thangka

ABSTRACT Detecting ritual implements in Thangka paintings—such as swords and scriptures—remains challenging due to their intricate visual composition and symbolic complexity. Existing object detection models, typically trained on natural scenes, tend to perform poorly in this domain. To address this limitation, we summarize the principles of composition in Thangka and identify key spatial and co‐occurrence priors specific to ritual implements. Based on these insights, we propose GPCDet: a guided by principles of composition detector that integrates domain‐specific priors into the detection process. Specifically, we introduce a spatial coordinate attention module to emphasize critical spatial regions where implements frequently appear. In addition, we design a graph convolution network‐auxiliary detection module to model inter‐category co‐occurrence, thereby enhancing feature representation and improving classification performance. Experiments on the newly curated ritual implements in Thangka (RITK) dataset show that GPCDet achieves substantial improvements over existing methods, establishing a new state‐of‐the‐art baseline for this challenging task.