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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2023.3269982
|2 doi
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|a pubmed24n1187.xml
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|a DE-627
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|a eng
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1 |
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|a Liang, Zhiyuan
|e verfasserin
|4 aut
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|a Multi-Granularity Context Network for Efficient Video Semantic Segmentation
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 04.06.2023
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|a Date Revised 04.06.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Current video semantic segmentation tasks involve two main challenges: how to take full advantage of multi-frame context information, and how to improve computational efficiency. To tackle the two challenges simultaneously, we present a novel Multi-Granularity Context Network (MGCNet) by aggregating context information at multiple granularities in a more effective and efficient way. Our method first converts image features into semantic prototypes, and then conducts a non-local operation to aggregate the per-frame and short-term contexts jointly. An additional long-term context module is introduced to capture the video-level semantic information during training. By aggregating both local and global semantic information, a strong feature representation is obtained. The proposed pixel-to-prototype non-local operation requires less computational cost than traditional non-local ones, and is video-friendly since it reuses the semantic prototypes of previous frames. Moreover, we propose an uncertainty-aware and structural knowledge distillation strategy to boost the performance of our method. Experiments on Cityscapes and CamVid datasets with multiple backbones demonstrate that the proposed MGCNet outperforms other state-of-the-art methods with high speed and low latency
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|a Journal Article
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1 |
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|a Dai, Xiangdong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wu, Yiqian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jin, Xiaogang
|e verfasserin
|4 aut
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| 700 |
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|a Shen, Jianbing
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 32(2023) vom: 10., Seite 3163-3175
|w (DE-627)NLM09821456X
|x 1941-0042
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|g volume:32
|g year:2023
|g day:10
|g pages:3163-3175
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|u http://dx.doi.org/10.1109/TIP.2023.3269982
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