Adaptive Semantic Information Extraction of Tibetan Opera Mask with Recall Loss

With the development of artificial intelligence, natural language processing enables us to better understand and utilize semantic information. However, traditional object detection algorithms cannot get an effective performance, when dealed with Tibetan opera mask datasets which have the properties of limited samples, symmetrical patterns and high inter-class distances. In order to solve this issue, we propose a novel feature representation model with recall loss function for detecting different marks. In the model, we develop an adaptive feature extraction network with fused layers to extract features. Furthermore, a lightweight efficient attention mechanism is designed to enhance the significance of key features. Additionally, a recall loss function is proposed to increase the differences among classes. Finally, experimental results on the dataset of Tibetan opera mask demonstrate that our proposed model outperforms compared models.