Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training
抄録
<jats:p>We propose Unicoder-VL, a universal encoder that aims to learn joint representations of vision and language in a pre-training manner. Borrow ideas from cross-lingual pre-trained models, such as XLM (Lample and Conneau 2019) and Unicoder (Huang et al. 2019), both visual and linguistic contents are fed into a multi-layer Transformer (Vaswani et al. 2017) for the cross-modal pre-training, where three pre-trained tasks are employed, including Masked Language Modeling(MLM), Masked Object Classification(MOC) and Visual-linguistic Matching(VLM). The first two tasks learn context-aware representations for input tokens based on linguistic and visual contents jointly. The last task tries to predict whether an image and a text describe each other. After pretraining on large-scale image-caption pairs, we transfer Unicoder-VL to caption-based image-text retrieval and visual commonsense reasoning, with just one additional output layer. We achieve state-of-the-art or comparable results on both two tasks and show the powerful ability of the cross-modal pre-training.</jats:p>
収録刊行物
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- Proceedings of the AAAI Conference on Artificial Intelligence
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Proceedings of the AAAI Conference on Artificial Intelligence 34 (07), 11336-11344, 2020-04-03
Association for the Advancement of Artificial Intelligence (AAAI)