What distinguishes instance segmentation from semantic segmentation?

Semantic segmentation classifies each pixel in an image into a specific category, providing a pixel-level understanding of the scene. It assigns a class label to every pixel, grouping pixels that belong to the same object class together. Instance segmentation, on the other hand, not only classifies each pixel but also distinguishes between different instances of the same object class. It identifies individual objects within a class and assigns a unique label to each instance. For example, in an image with three cats, semantic segmentation would label all cat pixels as 'cat', without differentiating between the individual cats. Instance segmentation would label each cat with a unique identifier, such as 'cat1', 'cat2', and 'cat3', allowing the model to distinguish between them. Therefore, instance segmentation provides a more detailed and granular understanding of the scene compared to semantic segmentation, enabling the model to identify and segment individual objects, even if they belong to the same class. Instance segmentation is typically more complex and computationally expensive than semantic segmentation because it requires not only classifying pixels but also grouping them into individual object instances.