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- W3207968478 abstract "Currently, the existing salient object detection methods based on convolutional neural networks commonly resort to constructing discriminative networks to aggregate high level and low level features. However, the potential contextual information is always difficult to be fully and reasonably utilized, which usually causes either the absence of useful features or the contamination of redundant features. To address these issues, we propose a novel ladder context correlation complementary network (LC3Net) in this paper, which is equipped with three crucial components. At the beginning, we propose a filterable convolution block (FCB) to assist the automatic collection of information on the diversity of initial features by skillfully inserting the atrous convolutions into independent branches. Besides, we propose a dense cross module (DCM) to facilitate the intimate aggregation of different levels of features by validly integrating the semantic information and detailed information of both adjacent and non-adjacent layers. Furthermore, we propose a bidirectional compression decoder (BCD) to help the progressive shrinkage of multi-scale features from coarse to fine by elaborately leveraging multiple pairs of alternating top-down and bottom-up feature interaction flows. Extensive experiments demonstrate the superiority of our method against 20 state-of-the-art methods." @default.
- W3207968478 created "2021-10-25" @default.
- W3207968478 creator A5004537379 @default.
- W3207968478 creator A5016051584 @default.
- W3207968478 creator A5049931230 @default.
- W3207968478 creator A5060658571 @default.
- W3207968478 date "2022-04-01" @default.
- W3207968478 modified "2023-09-26" @default.
- W3207968478 title "LC<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML display=inline id=d1e2414 altimg=si9.svg><mml:msup><mml:mrow /><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math>Net: Ladder context correlation complementary network for salient object detection" @default.
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- W3207968478 doi "https://doi.org/10.1016/j.knosys.2022.108372" @default.
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