Squeeze-and-Attention Networks for Semantic Segmentation | CVPR2020
Squeeze-and-Attention Networks for Semantic Segmentation 語義分割的壓縮注意力網絡。對SE改進使適用於語義分割 Abstrct： 當前語義分割方向注意力機制的聚合，由於更多的信息特徵增強了性能。然而，注意力機制忽視了語義分割的隱藏任務（pixel grouping），並受限於卷積核的網絡形狀。本文我們提出了一個新的squeeze-and-at
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相關文章
- 1. 【CVPR2020】Semi-Supervised Semantic Image Segmentation with Self-correcting Networks
- 2. FCN：Fully convolutional networks for semantic segmentation
- 3. FCN—— Fully Convolutional Networks for Semantic Segmentation
- 4. Fully Convolutional Adaptation Networks for Semantic Segmentation
- 5. Fully Convolutional Networks for Semantic Segmentation
- 6. FCN-Fully Convolutional Networks for Semantic Segmentation
- 7. 【Semantic segmentation】Fully Convolutional Networks for Semantic Segmentation 論文解析
- 8. 深度估計 Fully Convolutional Networks for Semantic Segmentation
- 9. ICCV2019-Asymmetric Non-local Neural Networks for Semantic Segmentation
- 10. 讀書筆記《Fully Convolutional Networks for Semantic Segmentation》