RGB-D Scene Recognition based on Object-Scene Relation

被引:0
|
作者
Guo, Yuhui [1 ]
Liang, Xun [1 ]
机构
[1] Renmin Univ China, 59 Zhongguancun Rd, Beijing RENMIN UNIV, Peoples R China
来源
THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | 2021年 / 35卷
基金
中国国家自然科学基金;
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We develop a RGB-D scene recognition model based on object-scene relation(RSBR). First learning a Semantic Network in the semantic domain that classifies the label of a scene on the basis of the labels of all object types. Then, we design an Appearance Network in the appearance domain that recognizes the scene according to local captions. We enforce the Semantic Network to guide the Appearance Network in the learning procedure. Based on the proposed RSBR model, we obtain the state-of-the-art results of RGB-D scene recognition on SUN RGB-D and NYUD2 datasets.
引用
收藏
页码:15787 / 15788
页数:2
相关论文
共 50 条
  • [1] RGB-D Scene Recognition based on Object-Scene Relation and Semantics-Preserving Attention
    Guo, Yuhui
    Liang, Xun
    PROCEEDINGS OF THE 2021 INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL (ICMR '21), 2021, : 127 - 134
  • [2] RGB-D Scene Recognition with Object-to-Object Relation
    Song, Xinhang
    Chen, Chengpeng
    Jiang, Shuqiang
    PROCEEDINGS OF THE 2017 ACM MULTIMEDIA CONFERENCE (MM'17), 2017, : 600 - 608
  • [3] Contextual object category recognition for RGB-D scene labeling
    Ali, Haider
    Shafait, Faisal
    Giannakidou, Eirini
    Vakali, Athena
    Figueroa, Nadia
    Varvadoukas, Theodoros
    Mavridis, Nikolaos
    ROBOTICS AND AUTONOMOUS SYSTEMS, 2014, 62 (02) : 241 - 256
  • [4] Image Representations With Spatial Object-to-Object Relations for RGB-D Scene Recognition
    Song, Xinhang
    Jiang, Shuqiang
    Wang, Bohan
    Chen, Chengpeng
    Chen, Gongwei
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 29 : 525 - 537
  • [5] Indoor Scene Recognition from RGB-D Images by Learning Scene Bases
    Wan, Shaohua
    Hu, Changbo
    Aggarwal, J. K.
    2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2014, : 3416 - 3421
  • [6] A method proposal of scene recognition for RGB-D cameras
    Danciu, Gabriel-Mihail
    2016 IEEE 11TH INTERNATIONAL SYMPOSIUM ON APPLIED COMPUTATIONAL INTELLIGENCE AND INFORMATICS (SACI), 2016, : 301 - 304
  • [7] Learning Effective RGB-D Representations for Scene Recognition
    Song, Xinhang
    Jiang, Shuqiang
    Herranz, Luis
    Chen, Chengpeng
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 28 (02) : 980 - 993
  • [8] Motion-Based Object Segmentation Based on Dense RGB-D Scene Flow
    Shao, Lin
    Shah, Parth
    Dwaracherla, Vikranth
    Bohg, Jeannette
    IEEE ROBOTICS AND AUTOMATION LETTERS, 2018, 3 (04): : 3797 - 3804
  • [9] MSN: Modality separation networks for RGB-D scene recognition
    Xiong, Zhitong
    Yuan, Yuan
    Wang, Qi
    NEUROCOMPUTING, 2020, 373 : 81 - 89
  • [10] Translate-to-Recognize Networks for RGB-D Scene Recognition
    Du, Dapeng
    Wang, Limin
    Wang, Huiling
    Zhao, Kai
    Wu, Gangshan
    2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, : 11828 - 11837