We show that a fully convolutional network (FCN), trained end-to-end, pixels-to-pixels on semantic segmentation exceeds the state-of-the-art without further machinery. To our knowledge, this is the first work to train FCNs end-to-end (1) for pixelwise prediction and (2) from supervised pre-training. Fully convolutional versions of existing networks predict dense outputs from arbitrary-sized inputs. Both learning and inference are performed whole-image-ata-time by dense feedforward computation and backpropagation. In-network upsampling layers enable pixelwise prediction and learning in nets with subsampled pooling.怎么翻译

我们展示了一种完全卷积网络（FCN），通过端到端的像素级别语义分割训练，超过了现有的最先进技术，而无需其他改进。据我们所知，这是第一项将FCN从监督预训练开始端到端地进行像素级别预测的工作。现有网络的完全卷积版本可以从任意大小的输入中预测密集输出。学习和推断都通过密集的前向计算和反向传播一次处理整个图像完成。网络内部的上采样层可以在进行子采样池化的网络中进行像素级别的预测和学习。