* '''Semantic segmentation''' is an approach detecting, for every pixel, the belonging class of the object.<ref>{{Cite journal|last1=Guo|first1=Dazhou|last2=Pei|first2=Yanting|last3=Zheng|first3=Kang|last4=Yu|first4=Hongkai|last5=Lu|first5=Yuhang|last6=Wang|first6=Song|date=2020|title=Degraded Image Semantic Segmentation With Dense-Gram Networks|journal=IEEE Transactions on Image Processing|volume=29|pages=782–795|doi=10.1109/TIP.2019.2936111|pmid=31449020|bibcode=2020ITIP...29..782G|s2cid=201753511|issn=1057-7149|doi-access=free}}</ref> For example, whenin alla figure with many people, inall athe figurepixels arebelonging segmentedto aspersons onewill objecthave the same class id and the pixels in the background aswill onebe objectclassified as background.
* '''Instance segmentation''' is an approach that identifies, for every pixel, athe specific belonging instance of the object. It detects each distinct object of interest in the image.<ref>{{Cite journal|last1=Yi|first1=Jingru|last2=Wu|first2=Pengxiang|last3=Jiang|first3=Menglin|last4=Huang|first4=Qiaoying|last5=Hoeppner|first5=Daniel J.|last6=Metaxas|first6=Dimitris N.|date=July 2019|title=Attentive neural cell instance segmentation|journal=Medical Image Analysis|language=en|volume=55|pages=228–240|doi=10.1016/j.media.2019.05.004|pmid=31103790|s2cid=159038604}}</ref> For example, when each person in a figure is segmented as an individual object.
* '''Panoptic segmentation''' combines both semantic and instance segmentation. Like semantic segmentation, panoptic segmentation is an approach that identifies, for every pixel, the belonging class. UnlikeMoreover, semanticlike in instance segmentation, panoptic segmentation distinguishes different instances of the same class.<ref name="Panoptic Segmentation">{{cite arXiv|authors=Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, Piotr Dollár|title=Panoptic Segmentation|eprint=1801.00868|class=cs.CV|year=2018}}</ref>
