  Instance segmentation is a challenging task aiming at classifying and
segmenting all object instances of specific classes. While two-stage box-based
methods achieve top performances in the image domain, they cannot easily extend
their superiority into the video domain. This is because they usually deal with
features or images cropped from the detected bounding boxes without alignment,
failing to capture pixel-level temporal consistency. We embrace the observation
that bottom-up methods dealing with box-free features could offer accurate
spacial correlations across frames, which can be fully utilized for object and
pixel level tracking. We first propose our bottom-up framework equipped with a
temporal context fusion module to better encode inter-frame correlations.
Intra-frame cues for semantic segmentation and object localization are
simultaneously extracted and reconstructed by corresponding decoders after a
shared backbone. For efficient and robust tracking among instances, we
introduce an instance-level correspondence across adjacent frames, which is
represented by a center-to-center flow, termed as instance flow, to assemble
messy dense temporal correspondences. Experiments demonstrate that the proposed
method outperforms the state-of-the-art online methods (taking image-level
input) on the challenging Youtube-VIS dataset.
