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.
@cite_21: 
@cite_12: Inspired by recent advances of deep learning in instance segmentation and object tracking, we introduce the concept of convnet-based guidance applied to video object segmentation. Our model proceeds on a per-frame basis, guided by the output of the previous frame towards the object of interest in the next frame. We demonstrate that highly accurate object segmentation in videos can be enabled by using a convolutional neural network (convnet) trained with static images only. The key component of our approach is a combination of offline and online learning strategies, where the former produces a refined mask from the previous frame estimate and the latter allows to capture the appearance of the specific object instance. Our method can handle different types of input annotations such as bounding boxes and segments while leveraging an arbitrary amount of annotated frames. Therefore our system is suitable for diverse applications with different requirements in terms of accuracy and efficiency. In our extensive evaluation, we obtain competitive results on three different datasets, independently from the type of input annotation.
@cite_49: We present an integrated probabilistic model for layered object tracking that combines dynamics on implicit shape representations, topological shape constraints, adaptive appearance models, and layered flow. The generative model combines the evolution of appearances and layer shapes with a Gaussian process flow and explicit layer ordering. Efficient MCMC sampling algorithms are developed to enable a particle filtering approach while reasoning about the distribution of object boundaries in video. We demonstrate the utility of the proposed tracking algorithm on a wide variety of video sources while achieving state-of-the-art results on a boundary-accurate tracking dataset.
Fusion Methods. The joint problem of video segmentation and flow estimation has been studied by layered models @cite_49 @cite_21 . Nevertheless, such methods rely on complicated optimization during inference, thereby limiting their applications. Recently, significant efforts have been made along the direction of video object segmentation while considering optical flow. In @cite_12 , a network that uses pre-computed optical flow as an additional input to improve segmentation results is developed. Different from this work, our model only requires images as the input, and we aim to jointly learn useful motion representations to help segmentation.