Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes（FRRN））
论文：https://arxiv.org/abs/1611.08323 代码：https://github.com/TobyPDE/FRRN 摘要 语义图像分割是现代自动驾驶系统的重要组成部分，因为对周围场景的精确理解对导航和行动规划至关重要。 论文提出了一种新的ResNet类架构，展现出强大的定位和识别性能。通过在网络中使用两个处理流，将多尺度上下文与像素级准确性结合在一起：一个流以全图像分辨率
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相关文章
- 1. 语义分割--(FRRN)Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
- 2. 语义分割--Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
- 3. 18-DenseASPP-for-Semantic-Segmentation-in-Street-Scenes
- 4. Semantic Segmentation---DenseASPP for Semantic Segmentation in Street Scenes（论文解读十一）
- 5. DenseASPP for Semantic Segmentation in Street Scenes阅读笔记
- 6. 【图像分割论文阅读】DenseASPP for Semantic Segmentation in Street Scenes
- 7. 语义分割--(DenseASPP )DenseASPP for Semantic Segmentation in Street Scenes
- 8. Fully Convolutional Adaptation Networks for Semantic Segmentation
- 9. FCN—— Fully Convolutional Networks for Semantic Segmentation
- 10. Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes解读