Abstract
The design innovation of textile and apparel is the core of enterprise competitiveness. By assessing the value of intellectual property rights such as textile and garment design copyrights, it can not only stimulate the innovation motivation of designers, but also realize the continuous value-added and market expansion of clothing brands, thereby promoting the development of innovative design in the textile and garment industry. In this paper, inspired by the artificial intelligence and computational model, a method based on a multimodal cultural product value assessment hybrid expert model (MCP-MOE for short) is proposed for textile and apparel design. First, a multimodal representation learning mechanism is designed to learn the multidimensional features of cultural products, by fusing multimodal data and extracting multidimensional features, to realize the complementary advantages of cultural product features. Second, we construct a MCP-MOE model to regressively train the above multidimensional features, which can dynamically obtain the key features of cultural products more objectively, without relying on subjective factors. Finally, in order to verify the effectiveness of the proposed method, the simulation experiments are simulated on the multimodal dataset, mean absolute error, mean squared error, and root mean squared error are used as the objective evaluation metrics, and compare our proposed method with existing methods such as XGBoost, LightGBM, and CatBoost. The extensive experimental results show the proposed method has a good performance in assessing cultural value, and makes sense in the study on the evaluation of cultural value.
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