Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/129920
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Type: Conference paper
Title: Architecture search of dynamic cells for semantic video segmentation
Author: Nekrasov, V.
Chen, H.
Shen, C.
Reid, I.D.
Citation: Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV '20), 2020, pp.1959-1968
Publisher: IEEE
Publisher Place: online
Issue Date: 2020
Series/Report no.: IEEE Winter Conference on Applications of Computer Vision
ISBN: 9781728165530
ISSN: 2472-6737
2642-9381
Conference Name: IEEE Winter Conference on Applications of Computer Vision (WACV) (1 Mar 2020 - 5 Mar 2020 : Snowmass Village, CO, USA)
Statement of
Responsibility: 
Vladimir Nekrasov, Hao Chen, Chunhua Shen, Ian Reid
Abstract: In semantic video segmentation the goal is to acquire consistent dense semantic labelling across image frames. To this end, recent approaches have been reliant on manually arranged operations applied on top of static semantic segmentation networks – with the most prominent building block being the optical flow able to provide information about scene dynamics. Related to that is the line of research concerned with speeding up static networks by approximating expensive parts of them with cheaper alternatives, while propagating information from previous frames. In this work we attempt to come up with generalisation of those methods, and instead of manually designing contextual blocks that connect per-frame outputs, we propose a neural architecture search solution, where the choice of operations together with their sequential arrangement are being predicted by a separate neural network. We showcase that such generalisation leads to stable and accurate results across common benchmarks, such as CityScapes and CamVid datasets. Importantly, the proposed methodology takes only 2 GPU-days, finds high-performing cells and does not rely on the expensive optical flow computation.
Rights: ©2020 IEEE
DOI: 10.1109/WACV45572.2020.9093531
Grant ID: http://purl.org/au-research/grants/arc/CE140100016
Published version: https://ieeexplore.ieee.org/xpl/conhome/9087828/proceeding
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Computer Science publications

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