Efficient Long-Short Temporal Attention network for unsupervised Video Object Segmentation-Reference-Cited by-同舟云学术

Efficient Long-Short Temporal Attention network for unsupervised Video Object Segmentation

Published:2024-02 Issue: Volume:146 Page:110078
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Li Ping^ORCID,Zhang Yu,Yuan Li,Xiao Huaxin,Lin Binbin^ORCID,Xu Xianghua

Funder

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference57 articles.

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4. Xiankai Lu, Wenguan Wang, Chao Ma, Jianbing Shen, Ling Shao, Fatih Porikli, See More, Know More: Unsupervised video object segmentation with co-attention siamese networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 3623–3632.

5. Ge-Peng Ji, Keren Fu, Zhe Wu, Deng-Ping Fan, Jianbing Shen, Ling Shao, Full-duplex strategy for video object segmentation, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2021, pp. 4922–4933.

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