In recent years, the problem of misinformation on the web has become widespread across languages, countries and various social media platforms. One problem central to stopping the spread of misinformation is identifying claims and prioritising them for fact-checking. Although there has been much work on automated claim detection from text recently, the role of images and their variety still need to be explored. As posts and content shared on social media are often multimodal, it has become crucial to view the problem of misinformation and fake news from a multimodal perspective. In this chapter, first, we present an overview of existing claim detection methods and their limitations; second, we present a unimodal approach to identify check-worthy claims; third, and lastly, we introduce a dataset that takes both the image and text into account for detecting claims and benchmark recent multimodal models on the task.