Semantic segmentation has a wide array of applications such as scene understanding, autonomous driving, and robot manipulation tasks. While existing segmentation models have achieved good performance using bottom-up deep neural processing, this paper describes a novel deep learning architecture that integrates top-down and bottom-up processing. The resulting model achieves higher accuracy at a relatively low computational cost. In the proposed model, higher-level top-down information is transmitted to the lower layers through recurrent connections in an encoder and a decoder, and the recurrent connection weights are trained using backpropagation. Experiments on several benchmark datasets demonstrate that this use of top-down information improves the mean intersection over union by more than 3% compared with a state-of-the-art bottom-up only network using the CamVid, SUN-RGBD and PASCAL VOC 2012 benchmark datasets. Additionally, the proposed model is successfully applied to a dataset designed for robotic grasping tasks.

中文翻译：

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使用递归卷积-反卷积神经网络对自上而下和自下而上的视觉处理进行集成以进行语义分割

语义分割具有广泛的应用，例如场景理解，自动驾驶和机器人操纵任务。尽管现有的分割模型使用自下而上的深度神经处理取得了良好的性能，但本文介绍了一种新颖的深度学习体系结构，该体系结构将自顶向下和自底向上的处理集成在一起。所得模型以相对较低的计算成本实现了更高的精度。在提出的模型中，较高级别的自上而下的信息通过编码器和解码器中的循环连接传输到较低层，并且使用反向传播训练循环连接权重。在几个基准数据集上进行的实验表明，与使用CamVid，SUN-RGBD和PASCAL VOC的最先进的自下而上的纯网络相比，这种自上而下的信息的使用将联合的平均交集提高了3％以上。 2012年基准数据集。此外，所提出的模型已成功应用于为机器人抓紧任务设计的数据集。