Clothing parsing is a challenging task. In this task, there are two main inherent challenges, which are the fine-grained parsing of small clothing components and the missegmentation of similar clothing categories. To address these challenges, this paper proposes a phase contour enhancement network for clothing parsing. It achieves improved accuracy while maintaining a relatively small network complexity. Leveraging the distinctive features of phase maps, we introduce the phase contour enhancement attention (PCEA) module to augment the encoder’s object edge information. To further enrich the model’s feature extraction capacity, we present the dilated convolution pyramid (DCP) module, combining it with a lightweight decoder to achieve improved global context modeling. The synergistic integration of the PCEA and DCP modules enhances the network with remarkable capabilities in capturing intricate contours, effectively surmounting the challenges of fine-grained parsing for small clothing components and mitigating the missegmentation of similar clothing categories. Through extensive experiments on the CFP, Modanet, and DeepFashion2 datasets and further expansion experiments on the LIP dataset, our method demonstrates outstanding performance across multiple evaluation metrics, surpassing state-of-the-art methods for clothing parsing. In conclusion, our phase contour enhancement network exhibits remarkable performance in both clothing parsing tasks and extended human parsing tasks.

In recent years, the online selection of virtual clothing styles has been used to explore and expand diversified personal aesthetics, and it is also an overall reform and challenge to the clothing industry. Under the condition of the existing clothing style categories, this paper puts forward a style classification method combining fine-grained and coarse-grained techniques. Furthermore, a new deep neural network is proposed, which can improve the robustness of recognition and avoid the interference of image background through the pan learning and the background learning of image features. In order to study the relationship between the fine-grained attributes of clothing and the whole style, firstly, the clothing types are learned to realize the pre-training of model parameters. Secondly, through the transfer learning of the first stage of the pre-training model parameters, the model parameters are fine-tuned to make them more suitable for identifying the coarse-grained style types. Finally, a network structure based on the dual attention mechanism is proposed to improve the accuracy of final identification by adding different attention mechanisms at different stages of the network to enhance the performance of network features. In the experiment, we collected 50,000 images of 10 clothing styles to train and evaluate the models. The results show that the proposed classification method can effectively distinguish clothing styles and types.