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Pre-trained language models are still far from human performance in tasksthat need understanding of properties (e.g. appearance, measurable quantity)and affordances of everyday objects in the real world since the text lacks suchinformation due to reporting bias. In this work, we study whether integratingvisual knowledge into a language model can fill the gap. We investigate twotypes of knowledge transfer: (1) text knowledge transfer using image captionsthat may contain enriched visual knowledge and (2) cross-modal knowledgetransfer using both images and captions with vision-language trainingobjectives. On 5 downstream tasks that may need visual knowledge to solve theproblem, we perform extensive empirical comparisons over the presentedobjectives. Our experiments show that visual knowledge transfer can improveperformance in both low-resource and fully supervised settings.

Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-modal Knowledge Transfer