Rule training by score-based supervised contrastive learning for sketch explanation

Abstract

This paper presents a novel approach to explain scoring results of infant visual-motor integration sketches utilized in developmental tests by training predefined rules for each test item. To address the performance issues caused by limited data, we employ a pre-trained model that uses supervised contrastive learning based on item scores. To ensure effective training, a memory bank structure is proposed to accumulate diverse embeddings over multiple iterations and prevent the encoder that processes item information from being trained to prevent collapsing in the Siamese network. Experiments demonstrate that the proposed method improves performance in both score and rule inferences, achieving an accuracy of approximately 75.95% in rule inference. In addition, an ablation study validates the effectiveness of the proposed approach in enhancing performance, confirming its potential as a reliable tool for early developmental screenings and clinical assessments. As such, the proposed approach could enhance clinical decision-making by providing essential interpretability for developmental tests.