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- W4313012315 abstract "Fashion has greatly influenced people's dressing habits, while fashion weeks are feasts of fashion and gather the world's top designers and brands together. Researchers never stop studying fashion styles. However, there are two shortcomings when researchers analyse clothing style: first, they ignore the fine-grained design attributes of clothing, and second, they need a large amount of expert knowledge to annotate the dataset. We collect clothing images from fashion shows and propose a method for modelling the popular clothing style by combining fine-grained clothing attributes and using embedded topic model. We use the Resnet-50 neural network to identify the color and shape attributes of clothing, use the embedded topic model to explore the relationship between clothing styles and fashion elements, and finally use it to analyse fashion trends and calculate clothing similarity. The experimental results show that our method scores higher on evaluation metrics such as perplexity and NMI than traditional methods." @default.
- W4313012315 created "2023-01-05" @default.
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- W4313012315 date "2022-07-22" @default.
- W4313012315 modified "2023-10-16" @default.
- W4313012315 title "Style Analysis of Clothing From Fashion Shows Based on Deep Learning" @default.
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- W4313012315 doi "https://doi.org/10.1109/dsit55514.2022.9943872" @default.
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