具有自适应特征选择的动态图神经网络用于基于RGB-D的室内场景识别
Dynamic Graph Neural Network with Adaptive Features Selection for RGB-D Based Indoor Scene Recognition
AI总结：
本文提出动态图模型，通过自适应节点选择机制提取RGB和深度模态的关键局部特征，用于提升室内场景识别性能，实验表明其优于现有方法。
摘要：
多模态的色彩和深度，即RGB-D，在最近的室内场景识别研究中具有重要意义。在这样的数据表示中，深度图能够描述场景的3D结构和物体间的几何关系。先前的研究表明，两种模态的局部特征对提升识别准确性至关重要。然而，该领域仍存在如何自适应选择和有效利用这些关键局部特征的问题。本文提出了一种动态图模型，具有自适应节点选择机制，以解决上述问题。在该模型中，构建了一个动态图来建模物体和场景之间的关系，并提出了一种自适应节点选择方法，以从RGB和深度两种模态中提取关键局部特征用于图建模。随后，这些节点按三个不同层次进行分组，表示物体之间的近或远关系。此外，图模型会根据注意力权重动态更新。最后，更新和优化的RGB和深度模态特征被融合用于室内场景识别。实验在公开数据集SUN RGB-D和NYU Depth v2上进行。广泛的结果表明，与现有方法相比，本文的方法具有优越的性能，并证明所提出的方法能够有效利用RGB和深度两种模态的关键局部特征。
英文摘要：
Multi-modality of color and depth, i.e., RGB-D, is of great importance in recent research of indoor scene recognition. In this kind of data representation, depth map is able to describe the 3D structure of scenes and geometric relations among objects. Previous works showed that local features of both modalities are vital for promotion of recognition accuracy. However, the problem of adaptive selection and effective exploitation on these key local features remains open in this field. In this paper, a dynamic graph model is proposed with adaptive node selection mechanism to solve the above problem. In this model, a dynamic graph is built up to model the relations among objects and scene, and a method of adaptive node selection is proposed to take key local features from both modalities of RGB and depth for graph modeling. After that, these nodes are grouped by three different levels, representing near or far relations among objects. Moreover, the graph model is updated dynamically according to attention weights. Finally, the updated and optimized features of RGB and depth modalities are fused together for indoor scene recognition. Experiments are performed on public datasets including SUN RGB-D and NYU Depth v2. Extensive results demonstrate that our method has superior performance when comparing to state-of-the-arts methods, and show that the proposed method is able to exploit crucial local features from both modalities of RGB and depth.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
