Contrastive Learning-Based Cross-Modal Fusion for Product Form Imagery Recognition: A Case Study on New Energy Vehicle Front-End Design

Fine-grained feature extraction and affective semantic mapping remain significant challenges in product form analysis. To address these issues, this study proposes a contrastive learning-based cross-modal fusion approach for product form imagery recognition, using the front-end design of new energy vehicles (NEVs) as a case study. The proposed method first employs the Biterm Topic Model (BTM) and Analytic Hierarchy Process (AHP) to extract thematic patterns and compute weight distributions from consumer review texts, thereby identifying key imagery style labels. These labels are then leveraged for image annotation, facilitating the construction of a multimodal dataset. Next, ResNet-50 and Transformer architectures serve as the image and text encoders, respectively, to extract and represent multimodal features. To ensure effective alignment and deep fusion of textual and visual representations in a shared embedding space, a contrastive learning mechanism is introduced, optimizing cosine similarity between positive and negative sample pairs. Finally, a fully connected multilayer network is integrated at the output of the Transformer and ResNet with Contrastive Learning (TRCL) model to enhance classification accuracy and reliability. Comparative experiments against various deep convolutional neural networks (DCNNs) demonstrate that the TRCL model effectively integrates semantic and visual information, significantly improving the accuracy and robustness of complex product form imagery recognition. These findings suggest that the proposed method holds substantial potential for large-scale product appearance evaluation and affective cognition research. Moreover, this data-driven fusion underpins sustainable product form design by streamlining evaluation and optimizing resource use.