ICCV2019-Asymmetric Non-local Neural Networks for Semantic Segmentation
摘要： Non-Local模塊是一種特別有用的語義分割技術，但也因其難以進行計算和佔用GPU內存而受到批評。本文非對稱Non-local神經網絡引入到語義分割中，提出了非對稱金字塔非局部塊(APNB)和非對稱融合非局部塊(AFNB)兩種顯著的語義分割方法。APNB利用一個金字塔採樣模塊嵌入Non-local模塊，在不犧牲性能的前提下，極大地減少了計算和內存消耗。AFNB是由APNB演化而來的，在充
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相關文章
- 1. ANN:Asymmetric Non-local Neural Networks for Semantic Segmentation
- 2. Fully Convolutional Adaptation Networks for Semantic Segmentation
- 3. Fully Convolutional Networks for Semantic Segmentation
- 4. [ICCV2019論文閱讀]Asymmetric Non-local Neural Networks for Semantic Segmentation
- 5. FCN：Fully convolutional networks for semantic segmentation
- 6. FCN—— Fully Convolutional Networks for Semantic Segmentation
- 7. FCN-Fully Convolutional Networks for Semantic Segmentation
- 8. 【文獻閱讀】Asymmetric Non-local Neural Networks for Semantic Segmentation
- 9. 【Semantic segmentation】Fully Convolutional Networks for Semantic Segmentation 論文解析
- 10. 論文《Fully Convolutional Networks for Semantic Segmentation》