  This paper proposes an end-to-end trainable network, SegFlow, for
simultaneously predicting pixel-wise object segmentation and optical flow in
videos. The proposed SegFlow has two branches where useful information of
object segmentation and optical flow is propagated bidirectionally in a unified
framework. The segmentation branch is based on a fully convolutional network,
which has been proved effective in image segmentation task, and the optical
flow branch takes advantage of the FlowNet model. The unified framework is
trained iteratively offline to learn a generic notion, and fine-tuned online
for specific objects. Extensive experiments on both the video object
segmentation and optical flow datasets demonstrate that introducing optical
flow improves the performance of segmentation and vice versa, against the
state-of-the-art algorithms.
