Learning to Adapt Structured Output Space for Semantic Segmentation
論文地址：https://arxiv.org/abs/1802.10349 代碼地址：https://github.com/wasidennis/AdaptSegNet 首先是基於pixel-level的理由：一方面，與基於特徵描述圖像全局視覺信息的圖像分類不同，用於語義分割的高維特徵編碼了複雜的表示。因此，在特徵空間中進行自適應不一定是語義分割的最佳選擇。另一方面，雖然分割輸出是在低維空間中，但
相關文章
- 1. Learning to Adapt Structured Output Space for Semantic Segmentation——CVPR2018
- 2. 《AdaptSegNet：Learning to Adapt Structured Output Space for Semantic Segmentation》論文筆記
- 3. 論文域適應語義分割：Learning to Adapt Structured Output Space for Semantic Segmentation
- 4. Structured Knowledge Distillation for Semantic Segmentation
- 5. 【論文閱讀】Structured Knowledge Distillation for Semantic Segmentation
- 6. 《Learning Dynamic Routing for Semantic Segmentation》
- 7. Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
- 8. 【論文筆記】Learning Deconvolution Network for Semantic Segmentation
- 9. Learning Deconvolution Network for Semantic Segmentation
- 10. Adversarial learning for semi-supervised semantic segmentation
- 更多相關文章...
- • XSLT
- • Scala for循環 - Scala教程
- • Composer 安裝與使用
- • 爲了進字節跳動，我精選了29道Java經典算法題，帶詳細講解
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. Learning to Adapt Structured Output Space for Semantic Segmentation——CVPR2018
- 2. 《AdaptSegNet：Learning to Adapt Structured Output Space for Semantic Segmentation》論文筆記
- 3. 論文域適應語義分割：Learning to Adapt Structured Output Space for Semantic Segmentation
- 4. Structured Knowledge Distillation for Semantic Segmentation
- 5. 【論文閱讀】Structured Knowledge Distillation for Semantic Segmentation
- 6. 《Learning Dynamic Routing for Semantic Segmentation》
- 7. Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
- 8. 【論文筆記】Learning Deconvolution Network for Semantic Segmentation
- 9. Learning Deconvolution Network for Semantic Segmentation
- 10. Adversarial learning for semi-supervised semantic segmentation