## 3 The Proposed Method
### Dynamic Cross-Feature Fusion Model  
The dynamic cross-modal feature fusion (DCFF) module is proposed to effectively capture high-order interactions among heterogeneous modalities while adaptively weight ing each modality based on its contextual relevance. Traditional fusion strategies--such as static weighting or direct concatenation--lack the capability to dynamically adjust to variations in the input data, often resulting in suboptimal representation learning. In contrast, the DCFF module introduces a dynamic mechanism that generates modality-specific weights in response to changing input conditions, thereby enhancing the robustness and expressiveness of the fused features. Building upon the foundational work in [21], this study extends the fusion framework to accommodate three distinct types of modalities: visual features, textual inscription, and archeological metadata. The overall architecture of the DCFF module is illustrated in Figure 7.  
In multimodal fusion tasks, conventional methods often employ direct concatenation or static weighting strategies to combine information from heterogeneous modalities. While these approaches are straightforward to implement, they suffer from critical limitations: they lack the ability to dynamically adjust modal contributions based on contextual information and fail to model deep semantic interactions across modalities. These shortcomings are especially pronounced in highly heterogeneous and sparsely annotated domains, such as bone-stick inscription analysis, where traditional concatenation-based fusion often fails to fully exploit the complementary strengths of different modalities. To address these limitations, this study employs a Dynamic Cross-Feature Fusion (DCFF) module, which enables adaptive modeling and explicit cross-modal interaction, thereby significantly enhancing both the expressive capacity and discriminative power of the fused representation. The DCFF architecture is composed of four core components: Dynamic Weight Generation (DWG): Unlike static concatenation-based designs, the DWG module utilizes a sigmoid combination to dynamically generate a weight distribution across modalities. This mechanism allows the model to automatically evaluate and regulate the importance of each modality according to input content, thus enabling a more flexible and context-sensitive fusion strategy. Nonlinear Transformation of In-modal Features (NTIF): In traditional fusion, raw features are typically passed directly into the fusion layer without transformation, limiting intra-modal expressiveness. The NTIF module introduces dedicated fully connected layers and ReLU activation functions to construct modality-specific non-linear encoding paths, which is particularly beneficial for sparse and low-dimensional data types such as archeological metadata. Weighted Feature Interaction (WFI): Moving beyond passive fusion, the WFI layer performs dimension-wise weighted interactions to actively model high-order correlations between modalities. This mechanism is particu  
Figure 7: Architecture of the Dynamic Cross-Feature Fusion (DCFF) module. larly suited to tasks like bone-stick matching, where semantic mismatches between visual and textual modalities can be substantial due to their inherently different representations. Generation of Fusion Features (GFF): This component integrates the activated intra-modal features and the cross-modal interactive representations into a unified multimodal embedding using a deep nonlinear transformation network. Compared to simple concatenation, this approach provides substantially improved expressiveness and discriminability. In summary, the proposed DCFF module offers considerable advantages over traditional fusion methods by enabling context-aware dynamic weighting and explicit semantic interaction between modalities.