【圖像分割論文閱讀】LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
文章由美國普渡大學Abhishek Chaurasia、Eugenio Culurciello共同完成。發表於2017 IEEE Visual Communications and Image Processing (VCIP)，相較之前論文出處，收錄文章的會議聲名不顯。 論文地址:LinkNet: Exploiting Encoder Representations for Efficient
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相關文章
- 1. 圖像分割「LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation」
- 2. LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation論文閱讀
- 3. 語義分割--LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
- 4. 【圖像分割論文閱讀】Efficient ConvNet for Real-time Semantic Segmentation
- 5. 【圖像分割論文閱讀】DenseASPP for Semantic Segmentation in Street Scenes
- 6. 【圖像分割論文閱讀】The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
- 7. 論文閱讀（詳細）：ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation
- 8. 【圖像分割論文閱讀】RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
- 9. 【圖像分割論文閱讀】ENet：A Deep Neural Network Architecture for Real-Time Semantic Segmentation
- 10. 【圖像分割論文閱讀】ICNet for Real-Time Semantic Segmentation on High-Resolution Images