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Masked Vision-Language Transformer in Fashion

2022-10-27 01:44:08
Ge-Peng Ji, Mingcheng Zhuge, Dehong Gao, Deng-Ping Fan, Christos Sakaridis, Luc Van Gool
arXiv_CV
           
Abstract

We present a masked vision-language transformer (MVLT) for fashion-specific multi-modal representation. Technically, we simply utilize vision transformer architecture for replacing the BERT in the pre-training model, making MVLT the first end-to-end framework for the fashion domain. Besides, we designed masked image reconstruction (MIR) for a fine-grained understanding of fashion. MVLT is an extensible and convenient architecture that admits raw multi-modal inputs without extra pre-processing models (e.g., ResNet), implicitly modeling the vision-language alignments. More importantly, MVLT can easily generalize to various matching and generative tasks. Experimental results show obvious improvements in retrieval (rank@5: 17%) and recognition (accuracy: 3%) tasks over the Fashion-Gen 2018 winner Kaleido-BERT. Code is made available at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2210.15110

PDF

https://arxiv.org/pdf/2210.15110.pdf

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