I am working on semantic segmentation using deep learning, and I have met the terms: semantic segmentation
, instance detection
, object detection
and object segmentation
.
What is the differences between them?
I am working on semantic segmentation using deep learning, and I have met the terms: semantic segmentation
, instance detection
, object detection
and object segmentation
.
What is the differences between them?
Some of the usage of these terms is either subjective to the user or context-dependent, but as far as I can tell a plausible reading of these can be:
instance detection - given an instance (i.e. an image of a specific object) you need to detect it in an image / image set. Result can be either "Image i has instance X", a segmentation of the instance in all of its occurrences or anything in between.
object detection - depending on context can be the same as instance detection, or could mean that given a specific class of objects you want to detect all objects of this class that occur in an image / image set
object segmentation - take object detection and add segmentation of the object in the images it occurs in.
semantic segmentation - attempt to segment given image(s) into semantically interesting parts. This usually means pixel-labeling to a predefined class list.
Another question about image segmentation terminology can be found here and might be of some interest for you.