Learning Referring Video Object Segmentation from Weak Annotation

Understand

Referring video object segmentation (RVOS) is a task that aims to segment the target object in all video frames based on a sentence describing the object.

• Although existing RVOS methods have achieved significant performance, they depend on densely-annotated datasets, which are expensive and time-consuming to obtain.
• In this paper, we propose a new annotation scheme that reduces the annotation effort by 8 times, while providing sufficient supervision for RVOS.
• Our scheme only requires a mask for the frame where the object first appears and bounding boxes for the rest of the frames.