RGB-D Scene Recognition via Spatial-Related Multi-Modal Feature Learning

RGB-D image-based scene recognition has achieved significant performance improvement with the development of deep learning methods. While convolutional neural networks can learn high-semantic level features for object recognition, these methods still have limitations for RGB-D scene classification. One limitation is that how to learn better multi-modal features for the RGB-D scene recognition is still an open problem. Another limitation is that the scene images are usually not object-centric and with great spatial variability. Thus, vanilla full-image CNN features maybe not optimal for scene recognition. Considering these problems, in this paper, we propose a compact and effective framework for RGB-D scene recognition. Specifically, we make the following contributions: 1) A novel RGB-D scene recognition framework is proposed to explicitly learn the global modal-specific and local modal-consistent features simultaneously. Different from existing approaches, local CNN features are considered for the learning of modal-consistent representations; 2) key Feature Selection (KFS) module is designed, which can adaptively select important local features from the high-semantic level CNN feature maps. It is more efficient and effective than object detection and dense patch-sampling based methods, and; 3) a triplet correlation loss and a spatial-attention similarity loss are proposed for the training of KFS module. Under the supervision of the proposed loss functions, the network can learn import local features of two modalities with no need for extra annotations. Finally, by concatenating the global and local features together, the proposed framework can achieve new state-of-the-art scene recognition performance on the SUN RGB-D dataset and NYU Depth version 2 (NYUD v2) dataset.