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* '''Semantic segmentation''' is an approach detecting, for every pixel, the belonging class.<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, in a figure with many people, all the pixels belonging to persons will have the same class id and the pixels in the background will be classified as background.
* '''Instance segmentation''' is an approach that identifies, for every pixel, the 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|doi-access=free}}</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. Moreover, like 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>
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