mlsanityData to 7 Oct 2026

Masked Vision-Language Transformer in Fashion

arXiv 2210.15110 ↗First posted 27 Oct 2022cs.CV cs.CLcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 44 of its 61 references that are in the map. First posted on arXiv 27 Oct 2022.

Builds on

44 of 61 references.

References · 44

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32 more
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