智能感知与计算研究中心知识库

RGB-D indoor scene classification is an essential and challenging task. Although convolutional neural network (CNN) achieves excellent results on RGB-D object recognition, it has several limitations when extended towards RGB-D indoor scene classification. 1) The semantic cues such as objects of the indoor scene have high spatial variabilities. The spatially rigid global representation from CNN is suboptimal. 2) The cluttered indoor scene has lots of redundant and noisy semantic cues; thus discerning discriminative information among them should not be ignored. 3) Directly concatenating or summing global RGB and Depth information as presented in popular methods cannot fully exploit the complementarity between two modalities for complicated indoor scenarios. To address the above problems, we propose a novel unified framework named Multi-modal Attentive Pooling Network (MAPNet) in this paper. Two orderless attentive pooling blocks are constructed in MAPNet to aggregate semantic cues within and between modalities meanwhile maintain the spatial invariance. The Intra-modality Attentive Pooling (IAP) block aims to mine and pool discriminative semantic cues in each modality. The Cross-modality Attentive Pooling (CAP) block is extended to learn different contributions across two modalities, which further guides the pooling of the selected discriminative semantic cues of each modality. We further show that the proposed model is interpretable, which helps to understand mechanisms of both scene classification and multi-modal fusion in MAPNet. Extensive experiments and analysis on SUN RGB-D Dataset and NYU Depth Dataset V2 show the superiority of MAPNet over current state-of-the-art methods. (C) 2019 Elsevier Ltd. All rights reserved.