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What's the Difference Between Instance Segmentation and Semantic Segmentation?

Updated: Dec 25, 2023

Instance segmentation and semantic segmentation are two important tasks in computer vision. Here's a more detailed explanation of the differences between these two tasks:



Semantic Segmentation: The goal of semantic segmentation is to classify each pixel in an image into one of several predefined classes. The output of semantic segmentation is a label map, where each pixel is assigned a class label, but different instances of the same class are not distinguished from one another. For example, in an image of a city street, pixels can be classified as road, building, sidewalk, sky, etc. Semantic segmentation models typically use a convolutional neural network (CNN) architecture to learn the relationships between pixels and classes.





Instance Segmentation: Instance segmentation is a more advanced version of semantic segmentation. In addition to classifying each pixel in an image, instance segmentation also separates the instances of the same class in the image. The output of instance segmentation is a label map, where each instance of an object is assigned a unique label so that each instance can be tracked separately. For example, in an image of a city street with multiple cars, instance segmentation would not only classify pixels as "cars," but it would also identify each individual car as a separate instance. Instance segmentation models typically use object detection techniques, such as bounding box regression and non-maximum suppression, in combination with semantic segmentation to separate instances of the same class.



In summary, the main difference between semantic segmentation and instance segmentation is that semantic segmentation categorizes an image into classes, while instance segmentation categorizes the image and identifies each individual instance of those classes. Instance segmentation is a more challenging task than semantic segmentation, as it requires a deeper understanding of the image and a higher level of precision in segmenting objects.



There is Another: Panoptic Segmentation


Panoptic segmentation is a computer vision task that combines both semantic and instance segmentation. The goal of panoptic segmentation is to produce a segmentation map of an image that not only classifies each pixel into a set of predefined classes (semantic segmentation) but also separates each instance of those classes as a unique object (instance segmentation).


In other words, panoptic segmentation aims to provide a complete and unified segmentation of an image, where each pixel is assigned a class label, and each instance of that class is assigned a unique instance label. The resulting segmentation map is similar to a scene-parsing map, where the image is decomposed into a set of semantically meaningful and distinct objects.


Panoptic segmentation is a challenging task, as it requires a high level of accuracy in both semantic and instance segmentation, as well as the ability to seamlessly integrate the results of both tasks into a single map. Panoptic segmentation models typically use a combination of CNNs and object detection techniques to perform both semantic and instance segmentation and then merge the results into a panoptic map.


Panoptic segmentation is becoming increasingly important in computer vision, as it can be used in a wide range of applications, such as autonomous driving, robotics, and augmented reality, where a complete and unified understanding of the scene is critical.

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