DeepLab-v3:Rethinking Atrous Convolution for Semantic Image Segmentation
Rethinking Atrous Convolution for Semantic Image Segmentation 目前的問題： 當前DCNN在語義分割的兩個挑戰： 1由連續池化操作或卷積所導致的特徵分辨率降低，這使得DCNNs能夠在不斷增加的抽象特徵表示中學習。然而，這種局部圖像變換的不變性可能會妨礙密集的預測任務，因爲在這些任務中需要詳細的空間信息。 2另一個困難來自存在多個尺度的物體
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相關文章
- 1. Rethinking Atrous Convolution for Semantic Image Segmentation論文解
- 2. Encoder-Decoder-with-Atrous-Separable-Convolution-for-Semantic-Image-Segmentation
- 3. DeepLabv3:《Rethinking Atrous Convolution for Semantic Image Segmentataion》
- 4. 語義分割 DeepLabv3--Rethinking Atrous Convolution for Semantic Image Segmentation
- 5. 《Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation》論文筆記
- 6. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation (DeepLab v3+) 論文筆記
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- 10. 《Rethinking Atrous Convolution for Semantic Image Segmentation》論文筆記