One-Shot video object segmentation

Caelles S, Maninis KK, Pont-Tuset J, Leal-Taixe L, Cremers D, Van Gool L (2017)

Publication Type: Conference contribution

Publication year: 2017

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2017-January

Pages Range: 5320-5329

Conference Proceedings Title: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017

Event location: Honolulu, HI, USA

ISBN: 9781538604571

DOI: 10.1109/CVPR.2017.565

Abstract

This paper tackles the task of semi-supervised video object segmentation, i.e., the separation of an object from the background in a video, given the mask of the first frame. We present One-Shot Video Object Segmentation (OSVOS), based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotated object of the test sequence (hence one-shot). Although all frames are processed independently, the results are temporally coherent and stable. We perform experiments on two annotated video segmentation databases, which show that OSVOS is fast and improves the state of the art by a significant margin (79.8% vs 68.0%).

Involved external institutions

Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich CH Switzerland (CH) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Caelles, S., Maninis, K.-K., Pont-Tuset, J., Leal-Taixe, L., Cremers, D., & Van Gool, L. (2017). One-Shot video object segmentation. In Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 (pp. 5320-5329). Honolulu, HI, USA: Institute of Electrical and Electronics Engineers Inc..

MLA:

Caelles, S., et al. "One-Shot video object segmentation." Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA Institute of Electrical and Electronics Engineers Inc., 2017. 5320-5329.

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