论文标题
RGBD语义细分的基于注意的双重监督解码器
Attention-based Dual Supervised Decoder for RGBD Semantic Segmentation
论文作者
论文摘要
编码器模型已广泛用于RGBD语义分割，其中大多数是通过两流网络设计的。通常，从RGBD共同推理颜色和几何信息对语义分割有益。但是，大多数现有的方法都无法全面利用编码器和解码器中的多模式信息。在本文中，我们提出了一种基于注意力的双重监督解码器，用于RGBD语义分割。在编码器中，我们设计了一个简单但有效的基于注意力的多模式融合模块，以提取和融合深度多层配对的互补信息。为了了解更多强大的深层表示和丰富的多模式信息，我们引入了双分支解码器，以有效利用不同任务的相关性和互补提示。 NYUDV2和SUN-RGBD数据集的广泛实验表明，我们的方法可以针对最先进的方法实现出色的性能。
Encoder-decoder models have been widely used in RGBD semantic segmentation, and most of them are designed via a two-stream network. In general, jointly reasoning the color and geometric information from RGBD is beneficial for semantic segmentation. However, most existing approaches fail to comprehensively utilize multimodal information in both the encoder and decoder. In this paper, we propose a novel attention-based dual supervised decoder for RGBD semantic segmentation. In the encoder, we design a simple yet effective attention-based multimodal fusion module to extract and fuse deeply multi-level paired complementary information. To learn more robust deep representations and rich multi-modal information, we introduce a dual-branch decoder to effectively leverage the correlations and complementary cues of different tasks. Extensive experiments on NYUDv2 and SUN-RGBD datasets demonstrate that our method achieves superior performance against the state-of-the-art methods.
