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We present a new vision-language (VL) pre-training model dubbed Kaleido-BERT,which introduces a novel kaleido strategy for fashion cross-modalityrepresentations from transformers. In contrast to random masking strategy ofrecent VL models, we design alignment guided masking to jointly focus more onimage-text semantic relations. To this end, we carry out five novel tasks,i.e., rotation, jigsaw, camouflage, grey-to-color, and blank-to-color forself-supervised VL pre-training at patches of different scale. Kaleido-BERT isconceptually simple and easy to extend to the existing BERT framework, itattains new state-of-the-art results by large margins on four downstream tasks,including text retrieval (R@1: 4.03% absolute improvement), image retrieval(R@1: 7.13% abs imv.), category recognition (ACC: 3.28% abs imv.), and fashioncaptioning (Bleu4: 1.2 abs imv.). We validate the efficiency of Kaleido-BERT ona wide range of e-commerical websites, demonstrating its broader potential inreal-world applications.

Kaleido-BERT: Vision-Language Pre-training on Fashion Domain