Atrous convolution, also known as dilated convolution, is a technique used in convolutional neural networks to expand the receptive field without increasing the number of parameters or the amount of computation. It is particularly useful in tasks like semantic segmentation where capturing multi-scale context is important, as it allows for a larger field of view without downsampling the feature map resolution.