@inproceedings{22995dad459140a996ad48ad74425f90,
title = "Enhanced memory network for video segmentation",
abstract = "This paper proposes an Enhanced Memory Network (EMN) for semi-supervised video object segmentation. Space-Time Memory Networks has proven the effectiveness of the abundant use of guidance information. To further improve the accuracy of unknown and small targets, we propose to perform fined-grained segmentation based on the correlation attention map. We introduce a siamese network to obtain the semantic similarity and relevance between the tracking objects and the whole image. The feature map extracted from the siamese network on the cropped image is multiplied onto the whole feature map as the attention of proposal objects. Also, an ASPP module is employed to increase the semantic receptive filed to further improve the segmentation accuracy on different scale. Based on the multi-object combination and multi-scale ensemble, the proposed algorithm achieves the first place on the YouTube-VOS 2019 Semi-supervised Video Object Segmentation Challenge with a J\&F mean score of 81.8\%.",
keywords = "Memory network, Video object segmentation",
author = "Zhishan Zhou and Lejian Ren and Pengfei Xiong and Yifei Ji and Peisen Wang and Haoqiang Fan and Si Liu",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019 ; Conference date: 27-10-2019 Through 28-10-2019",
year = "2019",
month = oct,
doi = "10.1109/ICCVW.2019.00083",
language = "英语",
series = "Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "689--692",
booktitle = "Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019",
address = "美国",
}