Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143257
Title: MoNet : deep motion exploitation for video object segmentation
Authors: Xiao, Huaxin
Feng, Jiashi
Lin, Guosheng
Liu, Yu
Zhang, Maojun
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Xiao, H., Feng, J., Lin, G., Liu, Y. & Zhang, M. (2018). MoNet : deep motion exploitation for video object segmentation. Proceedings of the 2018 IEEE/CVF Conference o Computer Vision and Pattern Recognition (2018 CVPR). doi:10.1109/CVPR.2018.00125
Abstract: In this paper, we propose a novel MoNet model to deeply exploit motion cues for boosting video object segmentation performance from two aspects, i.e., frame representation learning and segmentation refinement. Concretely, MoNet exploits computed motion cue (i.e., optical flow) to reinforce the representation of the target frame by aligning and integrating representations from its neighbors. The new representation provides valuable temporal contexts for segmentation and improves robustness to various common contaminating factors, e.g., motion blur, appearance variation and deformation of video objects. Moreover, MoNet exploits motion inconsistency and transforms such motion cue into foreground/background prior to eliminate distraction from confusing instances and noisy regions. By introducing a distance transform layer, MoNet can effectively separate motion-inconstant instances/regions and thoroughly refine segmentation results. Integrating the proposed two motion exploitation components with a standard segmentation network, MoNet provides new state-of-the-art performance on three competitive benchmark datasets.
URI: https://hdl.handle.net/10356/143257
ISBN: 978-1-5386-6421-6
DOI: 10.1109/CVPR.2018.00125
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/CVPR.2018.00125.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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