  Segmenting objects in videos is a fundamental computer vision task. The
current deep learning based paradigm offers a powerful, but data-hungry
solution. However, current datasets are limited by the cost and human effort of
annotating object masks in videos. This effectively limits the performance and
generalization capabilities of existing video segmentation methods. To address
this issue, we explore weaker form of bounding box annotations.
  We introduce a method for generating segmentation masks from per-frame
bounding box annotations in videos. To this end, we propose a spatio-temporal
aggregation module that effectively mines consistencies in the object and
background appearance across multiple frames. We use our resulting accurate
masks for weakly supervised training of video object segmentation (VOS)
networks. We generate segmentation masks for large scale tracking datasets,
using only their bounding box annotations. The additional data provides
substantially better generalization performance leading to state-of-the-art
results in both the VOS and more challenging tracking domain.
