Han portrait stones are decorative stone carvings on funerary architectural structures，such as tombs and ancestral halls，dating back to the Han Dynasty. The contents of these stones depict mythological legends，institutional systems，and everyday life scenes，including chariot processions，kitchen banquets，textile farming，and other scenes that directly reflect the social practices of the Han period. Iconic figures，such as the Queen Mother of the West （Xiwangmu），Fuxi and Nüwa，and feathered immortals （Yuren），represent the Han people’s cosmology of “immortal souls”. In particular，Xiwangmu images are prevalent in the tombs of the middle and lower classes but seldom appear in noble tombs，reflecting hierarchical differences in beliefs. Themes，such as ancient sages and war scenes，which combine architectural practicality and artistic decoration，serve educational and commemorative functions，embodying realism and romanticism. This study proposes an image-text retrieval method that leverages semantic information from mask images of Han portrait stones to enhance images and texts，addressing the domain shift problem present in existing image-text retrieval methods. The method introduces a semantic enhancement module and integrates semantic information with randomly generated prompt templates for text reorganization. This design enables the model to learn additional discriminative features，understand different objects in images，align text with images，and improve the accuracy and comprehensiveness of image-text retrieval.

This study utilizes the architecture of Chinese contrastive language-image pre-training （Chinese CLIP） using a vision transformer （ViT） as the image encoder and robustly optimized bidirectional encoder representations from Transformers （RoBERTa） model as the text encoder to extract image and text features. Semantic information from mask images and original text is used to calculate object-specific weights. Attention weights are derived by combining these weights with features from mask and original images. After a residual connection，these attention weights are used to generate attention features，while the weights themselves serve as semantic enhancement features. The original image features，attention features，and enhancement features are combined through a multi-stage feature fusion strategy at a ratio of 0.4：0.5：0.1 to form composite features. This design enables the model to focus on the primary subjects and important objects in images while enriching feature representation without information loss. Effective semantic objects are extracted from the mask images，and the original texts are embedded into randomly generated prompt templates incorporating these objects to produce enhanced texts. This process structures text descriptions，strengthens semantic expression，and improves matching with image features. Furthermore，dynamic temperature adjustment and asymmetric similarity calculation are introduced： the contrastive learning temperature is adaptively adjusted based on feature similarity （low temperatures applied to high-similarity samples to enhance discriminative learning and high temperatures applied to low-similarity samples to increase tolerance）. The different temperature coefficients （0.9 for image-to-text and 1.1 for text-to-image） balance the matching difficulty in both directions，accounting for the distribution differences between image and text features to improve the accuracy and stability of cross-modal matching.

In this study，a series of comparative experiments，ablation experiments，and visual comparison experiments is conducted on the proposed model to evaluate the model’s performance and the contributions of each module. Results show that，on the Han portrait stone test set，the proposed method significantly improves the text-to-image retrieval metrics Mean_Recall （%），Recall@1 （%），Recall@5 （%），and Recall@10 （%） by 15.20%，21.84%，14.29%，and 9.48%，respectively，compared with the second-ranked bootstrapping language-image pre-training model in terms of precision and comprehensiveness. During the image-to-text retrieval，the metrics Mean_Recall （%），Recall@1 （%），Recall@5 （%），and Recall@10 （%） are improved by 17.47%，23.22%，18.45%，and 10.72%，respectively，compared with the second-ranked cross-modal vision-language model （X2-VLM），demonstrating significant improvements in image-text retrieval performance. Visualization experiments can effectively assist us in observing the improvement of the model’s performance and the enhanced effects brought about by each added module，making the experimental results highly intuitive. Additionally，the proposed method maintains good retrieval performance in data robustness experiments. The average recall rate of the model consistently exceeds 70%，and it maintains excellent retrieval performance. This outcome indicates that the proposed model exhibits remarkable data robustness and is applicable to various scenarios，such as fuzzy retrieval.

The proposed method enhances the model’s ability to align Han portrait stone images with their descriptive texts. The model can learn effective and crucial features by simultaneously enhancing text and images. The proposed dynamic temperature mechanism adapts to the structural characteristics of the Han portrait stone dataset and enhances the model’s ability to learn from difficult samples. effectively improving the performance of image-text retrieval in terms of accuracy and comprehensiveness. However，when objects in images are similar but differ in positional information，the image-text retrieval results may suffer from confusion and false positives. Given the limited scale and diversity of the dataset for Han portrait stone image-text retrieval，the model’s cross-modal retrieval performance may be constrained. In the future，we will optimize the model’s capability to semantically analyze the different positions and actions of objects in images and expand the Han portrait stone dataset.