This paper proposes a new method called Multimodal RNNs for RGB-D scene
semantic segmentation. It is optimized to classify image pixels given two input
sources: RGB color channels and Depth maps. It simultaneously performs training
of two recurrent neural networks (RNNs) that are crossly connected through
information transfer layers, which are learnt to adaptively extract relevant
cross-modality features. Each RNN model learns its representations from its own
previous hidden states and transferred patterns from the other RNNs previous
hidden states; thus, both model-specific and crossmodality features are
retained. We exploit the structure of quad-directional 2D-RNNs to model the
short and long range contextual information in the 2D input image. We carefully
designed various baselines to efficiently examine our proposed model structure.
We test our Multimodal RNNs method on popular RGB-D benchmarks and show how it
outperforms previous methods significantly and achieves competitive results
with other state-of-the-art works.

提出了一种新的多模态递归神经网络方法 (Multimodal RNNs) 用于 RGB-D 场景语义分割，它同时优化两个递归神经网络模型的训练，各自从自己以及另外一个模型的传递模式中学习特征，以提取相关的跨模态信息特征，实现了在 RGB 和深度图像数据上的显著优化和竞争性结果。