CASIA OpenIR
MAPNet: Multi-modal attentive pooling network for RGB-D indoor scene classification
Li, Yabei1,2,3; Zhang, Zhang1,2,3; Cheng, Yanhua4; Wang, Liang1,2,3,5; Tan, Tieniu1,2,3,5
Source PublicationPATTERN RECOGNITION
ISSN0031-3203
2019-06-01
Volume90Pages:436-449
Corresponding AuthorZhang, Zhang(zzhang@nlpr.ia.ac.cn)
AbstractRGB-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.
KeywordIndoor scene classification Multi-modal fusion RGB-D Attentive pooling
DOI10.1016/j.patcog.2019.02.005
WOS KeywordIMAGE FEATURES
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000463130400036
PublisherELSEVIER SCI LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23478
Collection中国科学院自动化研究所
Corresponding AuthorZhang, Zhang
Affiliation1.CASIA, CRIPAC, Beijing, Peoples R China
2.CASIA, NLPR, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Tencent WeChat AI, Beijing, Peoples R China
5.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Yabei,Zhang, Zhang,Cheng, Yanhua,et al. MAPNet: Multi-modal attentive pooling network for RGB-D indoor scene classification[J]. PATTERN RECOGNITION,2019,90:436-449.
APA Li, Yabei,Zhang, Zhang,Cheng, Yanhua,Wang, Liang,&Tan, Tieniu.(2019).MAPNet: Multi-modal attentive pooling network for RGB-D indoor scene classification.PATTERN RECOGNITION,90,436-449.
MLA Li, Yabei,et al."MAPNet: Multi-modal attentive pooling network for RGB-D indoor scene classification".PATTERN RECOGNITION 90(2019):436-449.
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