【圖像分割論文閱讀】DenseASPP for Semantic Segmentation in Street Scenes
本文由谷歌DeepMotion團隊發佈，發表於CVPR2018. 論文地址：DenseASPP for Semantic Segmentation in Street Scenes 背景 在自動駕駛領域，語義圖像分割是基本的街道場景理解任務，它要求給高分辨率圖像分每一個像素點進行分類.由於自動駕駛領域中場景中的目標尺度變化非常大.要求對高層次的特徵在多尺度上正確編碼.ASPP在獲得更大感受野的同時
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相關文章
- 1. Semantic Segmentation---DenseASPP for Semantic Segmentation in Street Scenes（論文解讀十一）
- 2. 語義分割--(DenseASPP )DenseASPP for Semantic Segmentation in Street Scenes
- 3. 18-DenseASPP-for-Semantic-Segmentation-in-Street-Scenes
- 4. DenseASPP for Semantic Segmentation in Street Scenes閱讀筆記
- 5. 語義分割--(FRRN)Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
- 6. 語義分割--Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
- 7. 【圖像分割論文閱讀】Efficient ConvNet for Real-time Semantic Segmentation
- 8. 【圖像分割論文閱讀】LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
- 9. Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes（FRRN））
- 10. 【圖像分割論文閱讀】RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation