We present an approach for object segmentation in videos that combines
frame-level object detection with concepts from object tracking and motion
segmentation. The approach extracts temporally consistent object tubes based on
an off-the-shelf detector. Besides the class label for each tube, this provides
a location prior that is independent of motion. For the final video
segmentation, we combine this information with motion cues. The method
overcomes the typical problems of weakly supervised/unsupervised video
segmentation, such as scenes with no motion, dominant camera motion, and
objects that move as a unit. In contrast to most tracking methods, it provides
an accurate, temporally consistent segmentation of each object. We report
results on four video segmentation datasets: YouTube Objects, SegTrackv2,
egoMotion, and FBMS.

该论文提出了一种用于视频中物体分割的方法，结合了帧级物体检测与物体跟踪、运动分割等概念，提取了基于现成检测器的时间一致性物体管，结合运动线索提供视频分割，克服了弱监督 / 无监督视频分割的典型问题，并提供每个对象的精确、时间一致的分割。