Weakly Supervised Semantic Segmentation with Boundary Exploration論文解讀
Weakly Supervised Semantic Segmentation with Boundary Exploration論文解讀 摘要：爲了在弱監督下獲得語義分割，本文提出了一種簡單而有效的方法，該方法基於從訓練圖像中顯式探索對象邊界以保持。 具體來說，我們通過利用從分割和邊界的一致率。具體來講，利用CNN分類器獲得的粗定位映射來合成邊界註釋，並使用註釋來訓練本文提出的稱爲BENet的網
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相關文章
- 1. 【論文筆記】Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing
- 2. 論文閱讀：Multi-Miner: Object-Adaptive Region Mining for Weakly-Supervised Semantic Segmentation
- 3. 【論文閱讀】Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation
- 4. Semantic Segmentation---DeepLab V1:Semantic Image Segmentation with Deep Convolutional Nets..（論文解讀二）
- 5. Semantic Segmentation -- (DeepLabv1)Semantic image segmentation with deep convolutional ... CRFs論文解讀
- 6. 【論文總結】weakly- and semi-supervised learning of a DCNN for semantic Image Segmentation
- 7. 【論文筆記】Simple Does It: Weakly Supervised Instance and Semantic Segmentation
- 8. 【CVPR2020】Semi-Supervised Semantic Image Segmentation with Self-correcting Networks
- 9. 《Self-Supervised Difference Detection for Weakly-Supervised Semantic Segmentation》筆記
- 10. 弱監督論文簡讀：Joint Learning of Saliency Detection and Weakly Supervised Semantic Segmentation