Convolutional Neural Networks (CNNs) are a type of deep learning neural network that have been particularly successful in the field of computer vision, especially in visual recognition tasks such as object detection, image classification, and image segmentation.

CNNs are designed to automatically learn and extract features from images. This is done through a process of convolution, where a set of filters (also known as kernels or weights) are applied to the input image to produce a set of feature maps. These feature maps capture different aspects of the image, such as edges, corners, and textures, and are then passed through activation functions such as ReLU to introduce non-linearity.

The feature maps are then passed through a pooling layer, which reduces the dimensionality of the feature maps and helps to make the network more robust to small translations and distortions in the input image. This process is repeated several times, with multiple convolutional and pooling layers, to extract increasingly abstract and high-level features.

Finally, the output of the last convolutional layer is flattened and passed through one or more fully connected layers, which are used to classify the image into different categories. The network is trained using backpropagation and gradient descent, where the weights of the filters are updated to minimize the difference between the predicted output and the ground truth labels.

Overall, CNNs have been very successful in a wide range of visual recognition tasks, achieving state-of-the-art performance on benchmark datasets such as ImageNet, CIFAR-10, and COCO. They are widely used in industry and academia for a variety of applications, including self-driving cars, medical image analysis, and surveillance systems.