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Multimodal tasks in the fashion domain have significant potential fore-commerce, but involve challenging vision-and-language learning problems -e.g., retrieving a fashion item given a reference image plus text feedback froma user. Prior works on multimodal fashion tasks have either been limited by thedata in individual benchmarks, or have leveraged generic vision-and-languagepre-training but have not taken advantage of the characteristics of fashiondata. Additionally, these works have mainly been restricted to multimodalunderstanding tasks. To address these gaps, we make two key contributions.First, we propose a novel fashion-specific pre-training framework based onweakly-supervised triplets constructed from fashion image-text pairs. We showthe triplet-based tasks are an effective addition to standard multimodalpre-training tasks. Second, we propose a flexible decoder-based modelarchitecture capable of both fashion retrieval and captioning tasks. Together,our model design and pre-training approach are competitive on a diverse set offashion tasks, including cross-modal retrieval, image retrieval with textfeedback, image captioning, relative image captioning, and multimodalcategorization.

FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified Retrieval and Captioning