What are the differences between “instance detection” and "semantic segmentation"?

Question

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?

Answer 1

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 (ie 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.