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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2022.3232209
|2 doi
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|a pubmed24n1184.xml
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|a (DE-627)NLM355229595
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|a (NLM)37018243
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Ma, Mingcan
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Boosting Broader Receptive Fields for Salient Object Detection
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|c 2023
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Revised 05.04.2023
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a Salient Object Detection has boomed in recent years and achieved impressive performance on regular-scale targets. However, existing methods encounter performance bottlenecks in processing objects with scale variation, especially extremely large- or small-scale objects with asymmetric segmentation requirements, since they are inefficient in obtaining more comprehensive receptive fields. With this issue in mind, this paper proposes a framework named BBRF for Boosting Broader Receptive Fields, which includes a Bilateral Extreme Stripping (BES) encoder, a Dynamic Complementary Attention Module (DCAM) and a Switch-Path Decoder (SPD) with a new boosting loss under the guidance of Loop Compensation Strategy (LCS). Specifically, we rethink the characteristics of the bilateral networks, and construct a BES encoder that separates semantics and details in an extreme way so as to get the broader receptive fields and obtain the ability to perceive extreme large- or small-scale objects. Then, the bilateral features generated by the proposed BES encoder can be dynamically filtered by the newly proposed DCAM. This module interactively provides spacial-wise and channel-wise dynamic attention weights for the semantic and detail branches of our BES encoder. Furthermore, we subsequently propose a Loop Compensation Strategy to boost the scale-specific features of multiple decision paths in SPD. These decision paths form a feature loop chain, which creates mutually compensating features under the supervision of boosting loss. Experiments on five benchmark datasets demonstrate that the proposed BBRF has a great advantage to cope with scale variation and can reduce the Mean Absolute Error over 20% compared with the state-of-the-art methods
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Xia, Changqun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xie, Chenxi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Xiaowu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Jia
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g PP(2023) vom: 04. Jan.
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
8 |
|g volume:PP
|g year:2023
|g day:04
|g month:01
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2022.3232209
|3 Volltext
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