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arXiv · 2210.15110

Masked Vision-Language Transformer in Fashion

Ge-Peng Ji·Mingcheng Zhuge·Dehong Gao·Deng-Ping Fan·Christos Sakaridis·Luc Van Gool
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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 https://github.com/GewelsJI/MVLT.

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Ge-Peng Ji, Mingcheng Zhuge, Dehong Gao, Deng-Ping Fan, Christos Sakaridis, Luc Van Gool. 2022-10-27. Masked Vision-Language Transformer in Fashion. https://doi.org/10.1007/s11633-022-1394-4

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