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Humans apprehend the world through various sensory modalities, yet languageis their predominant communication channel. Machine learning systems need todraw on the same multimodal richness to have informed discourses with humans innatural language; this is particularly true for systems specialized invisually-dense information, such as dialogue, recommendation, and searchengines for clothing. To this end, we train a visual question answering (VQA)system to answer complex natural language questions about apparel in fashionphotoshoot images. The key to the successful training of our VQA model is theautomatic creation of a visual question-answering dataset with 168 millionsamples from item attributes of 207 thousand images using diverse templates.The sample generation employs a strategy that considers the difficulty of thequestion-answer pairs to emphasize challenging concepts. Contrary to the recenttrends in using several datasets for pretraining the visual question answeringmodels, we focused on keeping the dataset fixed while training various modelsfrom scratch to isolate the improvements from model architecture changes. Wesee that using the same transformer for encoding the question and decoding theanswer, as in language models, achieves maximum accuracy, showing that visuallanguage models (VLMs) make the best visual question answering systems for ourdataset. The accuracy of the best model surpasses the human expert level, evenwhen answering human-generated questions that are not confined to the templateformats. Our approach for generating a large-scale multimodal domain-specificdataset provides a path for training specialized models capable ofcommunicating in natural language. The training of such domain-expert models,e.g., our fashion VLM model, cannot rely solely on the large-scalegeneral-purpose datasets collected from the web.

FashionVQA: A Domain-Specific Visual Question Answering System