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Deep Spatio-Temporal Random Fields for Efficient Video Segmentation.

Chandra, S; Couprie, C; Kokkinos, I; (2018) Deep Spatio-Temporal Random Fields for Efficient Video Segmentation. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Proceedings. (pp. pp. 8915-8924). IEEE: Salt Lake City, UT, USA. Green open access

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Abstract

In this work we introduce a time- and memory-efficient method for structured prediction that couples neuron decisions across both space at time. We show that we are able to perform exact and efficient inference on a densely-connected spatio-temporal graph by capitalizing on recent advances on deep Gaussian Conditional Random Fields (GCRFs). Our method, called VideoGCRF is (a) efficient, (b) has a unique global minimum, and (c) can be trained end-to-end alongside contemporary deep networks for video understanding. We experiment with multiple connectivity patterns in the temporal domain, and present empirical improvements over strong baselines on the tasks of both semantic and instance segmentation of videos. Our implementation is based on the Caffe2 framework and will be available at https://github.com/siddharthachandra/gcrf-v3.0.

Type: Proceedings paper
Title: Deep Spatio-Temporal Random Fields for Efficient Video Segmentation.
Event: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/CVPR.2018.00929
Publisher version: https://doi.org/10.1109/CVPR.2018.00929
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10089420
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