Masked Vision-language Transformer in Fashion

We present a masked vision-language transformer (MVLT) for fashion-specific multi-modal representation. Technically, we simply utilize the vision transformer architecture for replacing the bidirectional encoder representations from Transformers (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. The code is available at https://github.com/GewelsJI/MVLT.

Automated summary

This paper presents a masked vision-language transformer (MVLT) for fashion-specific multi-modal representation, which replaces the bidirectional encoder representations from Transformers (BERT) with the vision transformer architecture. It is the first end-to-end framework for the fashion domain and includes masked image reconstruction (MIR) for fine-grained understanding of fashion. MVLT is an extensible and convenient architecture that can handle raw multi-modal inputs without extra pre-processing models and shows improvements in retrieval and recognition tasks compared to Kaleido-BERT, the Fashion-Gen 2018 winner.