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• W3149700357 abstract "Peak Response Map (PRM) highlighting the discriminative regions can be extracted from a pre-trained classification network. We can accurately localize instances of each class with the help of these response maps. However, these maps cannot provide reliable information for segmentation even with off-the-shelf object proposals. This is because neither PRM nor the proposals know which regions can be regarded as a complete instance. In this paper, we tackle this problem by proposing an Instance-aware Cue-propagation Network (ICN) with a new proposal-matching strategy. In particular, the ICN aims to filter out background distractions and cover the complete instance, while our proposed proposal-matching strategy adds a re-balancing constraint on the contributions of multi-scale object proposals. Extensive experiments conducted on the PASCAL VOC 2012 dataset show the superior performance of our method over weakly-supervised state-of-the-arts for both semantic and instance segmentation." @default.
• W3149700357 created "2021-04-13" @default.
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• W3149700357 date "2021-08-01" @default.
• W3149700357 modified "2023-10-17" @default.
• W3149700357 title "Weakly supervised segmentation via instance-aware propagation" @default.
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• W3149700357 doi "https://doi.org/10.1016/j.neucom.2021.02.093" @default.
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