In simple words, a CNN (Convolutional Neural Network) "sees" or interprets visual information by analyzing patterns and features at different levels of abstraction. At the initial layers, the CNN captures low-level features like edges and textures. As the information flows through the network, higher-level features such as shapes, objects, and complex patterns are detected.

The CNN's ability to identify these features is derived from the learned weights and filters within its convolutional layers. By convolving these filters with the input image, the CNN can detect specific patterns and activate relevant neurons. This hierarchical process allows the CNN to understand and classify images based on the presence of different features, ultimately making predictions or performing tasks like image recognition or object detection.