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Song_Box-Driven_Class-Wise_Region_Masking_and_Filling_Rate_Guided_Loss_for_CVPR_2019_paper.pdf
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🔗 Source: openaccess.thecvf.com
📊 Size: 1.36 MB
👤 Author: Chunfeng Song, Yan Huang, Wanli Ouyang, Liang Wang
⬇️ Downloads: 182

Summary

Semantic segmentation has made significant progress with deep Fully Convolutional Networks (FCN), but its performance relies heavily on expensive and time-consuming pixel-level annotations. To address this, a box-driven class-wise masking model (BCM) and a filling rate guided adaptive loss (FR-Loss) are proposed for weakly supervised semantic segmentation. The BCM removes irrelevant regions of each class, while the FR-Loss helps the model ignore wrongly labeled pixels in proposals by calculating the mean filling rates of each class. The method is evaluated on the PASCAL VOC 2012 benchmark and achieves state-of-the-art results.

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Description

Semantic segmentation has made significant progress with deep Fully Convolutional Networks (FCN), but its performance relies heavily on expensive and...

Technical Information

  • File Format: PDF
  • File Size: 1.36 MB
  • Pages: 10
  • Language: EN
  • Author: Chunfeng Song, Yan Huang, Wanli Ouyang, Liang Wang
  • Total Downloads: 182
  • Last Updated: 5 hours ago

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