CLVOS23: A Long Video Object Segmentation Dataset for Continual Learning is a dataset for semantic segmentation, object detection, and semi supervised learning tasks. It is applicable or relevant across various domains.
The dataset consists of 13362 images with 284 labeled objects belonging to 5 different classes including person, dressage, rat, and other: car and dog.
Images in the CLVOS23 dataset have pixel-level semantic segmentation annotations. There are 13078 (98% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. Alternatively, the dataset could be split into 9 videos names: dressage (3589 images), blueboy (2406 images), parkour boy (1578 images), rat (1416 images), car (1109 images), skiing slalom (903 images), dog (891 images), skating (778 images), and skiing (692 images). The dataset was released in 2023 by the University of Waterloo, Canada.
