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UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning
[article]
2022
arXiv
pre-print
Existed pre-training methods either focus on single-modal tasks or multi-modal tasks, and cannot effectively adapt to each other. They can only utilize single-modal data (i.e. text or image) or limited multi-modal data (i.e. image-text pairs). In this work, we propose a unified-modal pre-training architecture, namely UNIMO, which can effectively adapt to both single-modal and multi-modal understanding and generation tasks. Large scale of free text corpus and image collections can be utilized to
arXiv:2012.15409v4
fatcat:woa3moustzc6nexs3ggg3acsdm