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|a 10.1109/TPAMI.2022.3211171
|2 doi
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|a eng
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| 100 |
1 |
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|a Sang, Shengtian
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Small-Object Sensitive Segmentation Using Across Feature Map Attention
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| 264 |
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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 10.04.2023
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|a Date Revised 23.10.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Semantic segmentation is an important step in understanding the scene for many practical applications such as autonomous driving. Although Deep Convolutional Neural Networks-based methods have significantly improved segmentation accuracy, small/thin objects remain challenging to segment due to convolutional and pooling operations that result in information loss, especially for small objects. This article presents a novel attention-based method called Across Feature Map Attention (AFMA) to address this challenge. It quantifies the inner-relationship between small and large objects belonging to the same category by utilizing the different feature levels of the original image. The AFMA could compensate for the loss of high-level feature information of small objects and improve the small/thin object segmentation. Our method can be used as an efficient plug-in for a wide range of existing architectures and produces much more interpretable feature representation than former studies. Extensive experiments on eight widely used segmentation methods and other existing small-object segmentation models on CamVid and Cityscapes demonstrate that our method substantially and consistently improves the segmentation of small/thin objects
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Zhou, Yuyin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Islam, Md Tauhidul
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xing, Lei
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 5 vom: 01. Mai, Seite 6289-6306
|w (DE-627)NLM098212257
|x 1939-3539
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|g volume:45
|g year:2023
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|g month:05
|g pages:6289-6306
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|u http://dx.doi.org/10.1109/TPAMI.2022.3211171
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