SVNet: Supervoxel Network for Video Oversegmentation-Reference-Cited by-同舟云学术

SVNet: Supervoxel Network for Video Oversegmentation

Published:2023-10-20 Issue: Volume: Page:368-376
ISSN:
Container-title:Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering
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Author:

Qi Yijie¹^ORCID,Yang Baorong¹^ORCID,Zhang Wenjing²^ORCID,Dong Xiao³^ORCID

Affiliation:

1. College of Computer Engineering, Jimei University, China

2. School of Mathematics and Computer Science, Zhejiang A&F University, China

3. Guangdong Provincial Key Laboratory IRADS, BNU-HKBU United International College, China

Funder

Scientific Research Start-up Fund Project of Jimei University

Natural Science Foundation of Fujian Province

Start-up Fund from BNU-HKBU United International College

General Program Funded by Fujian Provincial Department of Education

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3650400.3650460

Reference38 articles.

1. Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation

2. Look Before You Match: Instance Understanding Matters in Video Object Segmentation

3. Coarse-to-fine video instance segmentation with factorized conditional appearance flows. IEEE/CAA Journal of Automatica Sinica, 10(5);Qin Zheyun;IEEE,2023

4. Ruixiang Chen, Sheng Liu, Junhao Chen, Bingnan Guo, and Feng Zhang. 2023. VLKP: Video Instance Segmentation with Visual-Linguistic Knowledge Prompts. In Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, Rhodes Island, Greece,1-5.

5. Shao-Yuan Lo, Poojan Oza, Sumanth Chennupati, Alejandro Galindo, and Vishal M. Patel. 2023. Spatio-temporal pixel-level contrastive learning-based source-free domain adaptation for video semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Vancouver, Canada, 10534-10543.