Transformers that rely on the self-attention mechanism to capture global dependencies have dominated in natural language modelling and their use in other domains, e.g. speech processing, has shown great potential. The impressive results obtained on these domains leads computer vision researchers to apply transformers to visual data. However, the application of an architecture designed for sequential data is not straightforward for data represented as 2-D matrices. This chapter presents how Transformers were introduced in the domain of vision processing, challenging the historical Convolutional Neural Networks based approaches. After a brief reminder about historical methods in computer vision, namely convolution and self-attention, the chapter focuses on the modifications introduced in the Transformers architecture to deal with the peculiarities of visual data, using two different strategies. In a last part, recent work applying Transformer architecture in a multimodal context is also presented.