Segmentation from localization: a weakly supervised semantic segmentation method for resegmenting CAM
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17779-4.pdf
Reference68 articles.
1. Dai J, He K, Sun J (2015) Boxsup: exploiting bounding boxes to supervise convolutional networks for semantic segmentation. In: IEEE
2. Lee J, Yi J, Shin C, Yoon S (2021) Bbam: bounding box attribution map for weakly supervised semantic and instance segmentation
3. Oh Y, Kim B, Ham B (2021) Background-aware pooling and noise-aware loss for weakly-supervised semantic segmentation
4. Song C, Huang Y, Ouyang W, Wang L (2019) Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation. In: 2019 IEEE/CVF Conference on computer vision and pattern recognition (CVPR)
5. Liang Z, Wang T, Zhang X, Sun J, Shen J (2022) Tree energy loss: towards sparsely annotated semantic segmentation
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