An, Shumin;
Liao, Qingmin;
Lu, Zongqing;
Xue, Jing-Hao;
(2023)
Dual Correlation Network for Efficient Video Semantic Segmentation.
IEEE Transactions on Circuits and Systems for Video Technology
10.1109/tcsvt.2023.3298644.
(In press).
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Abstract
Video data bring a big challenge to semantic segmentation due to the large volume of data and strong inter-frame redundancy. In this paper, we propose a dual local and global correlation network tailored for efficient video semantic segmentation. It consists of three modules: 1) a local attention based module, which measures correlation and achieves feature aggregation in a local region between key frame and non-key frame; 2) a consistent constraint module, which considers long-range correlation among pixels from a global view for promoting intra-frame semantic consistency of non-key frame; and 3) a key frame decision module, which selects key frames adaptively based on the ability of feature transferring. Extensive experiments on the Cityscapes and Camvid video datasets demonstrate that our proposed method could reduce inference time significantly while maintaining high accuracy. The implementation is available at https://github.com/An01168/DCNVSS.
| Type: | Article |
|---|---|
| Title: | Dual Correlation Network for Efficient Video Semantic Segmentation |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/tcsvt.2023.3298644 |
| Publisher version: | https://doi.org/10.1109/TCSVT.2023.3298644 |
| Language: | English |
| Additional information: | This version is the author-accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Semantic segmentation, Semantics, Correlation, Feature extraction, Redundancy, Schedules, Predictive models |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10174386 |
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