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• W3195586359 abstract "Saliency detection is a computer vision task that has been studied for a long time. Although the recent methods continuously improve detection accuracy, most of them do not consider the interactions of different feature layers sufficiently, and deep- and shallow-level features are not fused effectively. To combine the advantages of multi-layer features for detection, we propose a novel multi-pathway feature integration network (MFINet) by extracting and fusing information from multiple feature layers. Different from the top-down and bottom-up fusion styles, our proposed feature fusion is divided into two stages and three pathways are finally formed. All three pathways integrate multiple original backbone feature layers, where they integrate features in three different orders. The diversity of feature fusion compensates for the limitations of the top-down and bottom-up fusion approaches, effectively preventing the loss of useful information in the transmission process. In addition, we propose a cross-feature hierarchical fusion module. This module combines the saliency cues in two feature layers adaptively and reduces the representation of redundant information. Therefore, multiple such modules are embedded in the MFINet to enable the network to further learn salient object information accurately. To strengthen the response of boundary information in the salient object information, we also propose a boundary information enhancement strategy. Unlike existing methods that use an independent branch to extract boundary features, we directly capture boundary features from a boundary map. Experimental results on 5 benchmark datasets show that our method achieves better performance in different scenarios, consistently outperforming 12 state-of-the-art methods." @default.
• W3195586359 created "2021-08-30" @default.
• W3195586359 creator A5003702543 @default.
• W3195586359 creator A5072825282 @default.
• W3195586359 date "2021-10-01" @default.
• W3195586359 modified "2023-10-17" @default.
• W3195586359 title "Multi-pathway feature integration network for salient object detection" @default.
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• W3195586359 doi "https://doi.org/10.1016/j.neucom.2021.08.082" @default.
• W3195586359 hasPublicationYear "2021" @default.
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