  Current state-of-the-art object detection and segmentation methods work well
under the closed-world assumption. This closed-world setting assumes that the
list of object categories is available during training and deployment. However,
many real-world applications require detecting or segmenting novel objects,
i.e., object categories never seen during training. In this paper, we present,
UVO (Unidentified Video Objects), a new benchmark for open-world class-agnostic
object segmentation in videos. Besides shifting the problem focus to the
open-world setup, UVO is significantly larger, providing approximately 8 times
more videos compared with DAVIS, and 7 times more mask (instance) annotations
per video compared with YouTube-VOS and YouTube-VIS. UVO is also more
challenging as it includes many videos with crowded scenes and complex
background motions. We demonstrated that UVO can be used for other
applications, such as object tracking and super-voxel segmentation, besides
open-world object segmentation. We believe that UVo is a versatile testbed for
researchers to develop novel approaches for open-world class-agnostic object
segmentation, and inspires new research directions towards a more comprehensive
video understanding beyond classification and detection.
