Semantic Segmentation vs. Instance Segmentation

Semantic segmentation and instance segmentation are both computer vision tasks that involve segmenting an image into different regions or objects. However, they differ in the level of granularity and detail they provide.

Semantic segmentation involves assigning a label or category to every pixel in an image based on its semantic meaning. For example, in an image of a street scene, semantic segmentation might label all the pixels corresponding to cars as "car", all the pixels corresponding to buildings as "building", and so on. Semantic segmentation does not differentiate between individual instances of an object, but rather provides a high-level understanding of the scene.

Instance segmentation, on the other hand, involves identifying and differentiating between individual instances of objects in an image. For example, in the same street scene, instance segmentation would not only label all the pixels corresponding to cars as "car", but would also differentiate between each individual car in the scene. This provides a much more detailed understanding of the objects in the scene, which can be useful in applications such as autonomous driving or object tracking.

In summary, semantic segmentation provides a high-level understanding of the objects in an image, while instance segmentation provides a more detailed understanding of individual instances of objects.