【CVPR2018】ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation
作者 摘要 語義分割網絡可以端到端訓練，並準確地在像素級對多種目標分類。本文提出一個可實時運行且結果準確的語義分割架構。本架構核心是一個新層，使用residual connections和factorized convolutions，得以保證準確和高效。在單卡Titan X可達83FPS，Jetson TX1(英偉達GPU開發板)可達7FPS。在公開數據集Cityscapes上準確率類似於s
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相關文章
- 1. 論文閱讀（詳細）：ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation
- 2. 【圖像分割論文閱讀】Efficient ConvNet for Real-time Semantic Segmentation
- 3. Context Encoding for Semantic Segmentation[CVPR2018]
- 4. Learning to Adapt Structured Output Space for Semantic Segmentation——CVPR2018
- 5. 【CVPR2018】MobileFaceNets Efficient CNNs for Accurate RealTime Face Verification on Mobile Devices
- 6. Context Encoding for Semantic Segmentation
- 7. 語義分割--LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
- 8. 《Dense Residual Network for Retinal Vessel Segmentation》
- 9. 輕量級實時語義分割：ENet & ERFNet
- 10. FCN-Fully Convolutional Networks for Semantic Segmentation