Self-Supervised Superpixel Correspondences For Video Object Segmentation-Reference-Cited by-同舟云学术

Self-Supervised Superpixel Correspondences For Video Object Segmentation

Published:2022-11-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2022 4th International Conference on Video, Signal and Image Processing
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Author:

Tan Clement¹^ORCID,Yeo Chai Kiat²^ORCID,Tan Cheston³^ORCID,Fernando Basura⁴^ORCID

Affiliation:

1. School of Computer Science and Engineering, Nanyang Technological University, Singapore and Agency for Science, Technology and Research (A*STAR), Singapore

2. School of Computer Science and Engineering, Nanyang Technological University, Singapore

3. Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR), Singapore

4. Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3577164.3577182

Reference26 articles.

1. Ting Chen , Simon Kornblith , Mohammad Norouzi , and Geoffrey Hinton . 2020 . A simple framework for contrastive learning of visual representations . In International conference on machine learning. PMLR, 1597–1607 . Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597–1607.

2. Fabio Galasso , Roberto Cipolla , and Bernt Schiele . 2012 . Video segmentation with superpixels . In Asian conference on computer vision. Springer, 760–774 . Fabio Galasso, Roberto Cipolla, and Bernt Schiele. 2012. Video segmentation with superpixels. In Asian conference on computer vision. Springer, 760–774.

3. Superpixel-based video object segmentation using perceptual organization and location prior

4. Momentum Contrast for Unsupervised Visual Representation Learning

5. Deep Residual Learning for Image Recognition