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Authors: Xiaotian Liu ; Lei Yang ; Xiaoyu Zhang and Xiaohui Duan

Affiliation: School of Electronics Engineering and Computer Science, Peking University, Beijing, China

Keyword(s): Video Segmentation, Attention Mechanism, Encoder Network.

Abstract: To improve the performance of segmentation networks on video streaming, most researchers now use optical-flow based method and non optical-flow CNN based method. The former suffers from heavy computational cost and high latency while the latter suffers from poor applicability and versatility. In this paper, we design a Partial Channel Memory Attention module (PCMA) to store and fuse time series features from video sequences.Then, we propose a Memory Attention ResNet50 network (MA-ResNet50) by combining the PCMA module with ResNet50, making it the first video based feature extraction encoder appliable for most of the currently proposed segmentation networks. For experiments, we combine our MA-ResNet50 with four acknowledged per-frame segmentation networks: DeeplabV3P, PSPNet, SFNet, and DNLNet. The results show that our MA-ResNet50 outperforms the original ResNet50 generally in these 4 networks on VSPW and CamVid. Our method also achieves state-of-the-art accuracy on CamVid. The code is avilable at https://github.com/xiaotianliu01/MA-Resnet50. (More)

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Paper citation in several formats:
Liu, X.; Yang, L.; Zhang, X. and Duan, X. (2022). MA-ResNet50: A General Encoder Network for Video Segmentation. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 79-86. DOI: 10.5220/0010800800003124

@conference{visapp22,
author={Xiaotian Liu. and Lei Yang. and Xiaoyu Zhang. and Xiaohui Duan.},
title={MA-ResNet50: A General Encoder Network for Video Segmentation},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={79-86},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010800800003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - MA-ResNet50: A General Encoder Network for Video Segmentation
SN - 978-989-758-555-5
IS - 2184-4321
AU - Liu, X.
AU - Yang, L.
AU - Zhang, X.
AU - Duan, X.
PY - 2022
SP - 79
EP - 86
DO - 10.5220/0010800800003124
PB - SciTePress

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