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.
|Title of host publication||Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|Publication status||Published - 2017 Dec 22|
|Event||16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy|
Duration: 2017 Oct 22 → 2017 Oct 29
|Name||Proceedings of the IEEE International Conference on Computer Vision|
|Other||16th IEEE International Conference on Computer Vision, ICCV 2017|
|Period||17/10/22 → 17/10/29|
Bibliographical noteFunding Information:
This work is supported in part by the NSF CAREER Grant #1149783 and NSF IIS Grant #1152576.
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition