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Fashion vision-language pre-training models have shown efficacy for a widerange of downstream tasks. However, general vision-language pre-training modelspay less attention to fine-grained domain features, while these features areimportant in distinguishing the specific domain tasks from general tasks. Wepropose a method for fine-grained fashion vision-language pre-training based onfashion Symbols and Attributes Prompt (FashionSAP) to model fine-grainedmulti-modalities fashion attributes and characteristics. Firstly, we proposethe fashion symbols, a novel abstract fashion concept layer, to representdifferent fashion items and to generalize various kinds of fine-grained fashionfeatures, making modelling fine-grained attributes more effective. Secondly,the attributes prompt method is proposed to make the model learn specificattributes of fashion items explicitly. We design proper prompt templatesaccording to the format of fashion data. Comprehensive experiments areconducted on two public fashion benchmarks, i.e., FashionGen and FashionIQ, andFashionSAP gets SOTA performances for four popular fashion tasks. The ablationstudy also shows the proposed abstract fashion symbols, and the attributeprompt method enables the model to acquire fine-grained semantics in thefashion domain effectively. The obvious performance gains from FashionSAPprovide a new baseline for future fashion task research.

FashionSAP: Symbols and Attributes Prompt for Fine-grained Fashion Vision-Language Pre-training