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Author (up) Xinhang Song; Shuqiang Jiang; Luis Herranz edit   pdf
doi  openurl
  Title Combining Models from Multiple Sources for RGB-D Scene Recognition Type Conference Article
  Year 2017 Publication 26th International Joint Conference on Artificial Intelligence Abbreviated Journal  
  Volume Issue Pages 4523-4529  
  Keywords Robotics and Vision; Vision and Perception  
  Abstract Depth can complement RGB with useful cues about object volumes and scene layout. However, RGB-D image datasets are still too small for directly training deep convolutional neural networks (CNNs), in contrast to the massive monomodal RGB datasets. Previous works in RGB-D recognition typically combine two separate networks for RGB and depth data, pretrained with a large RGB dataset and then fine tuned to the respective target RGB and depth datasets. These approaches have several limitations: 1) only use low-level filters learned from RGB data, thus not being able to exploit properly depth-specific patterns, and 2) RGB and depth features are only combined at high-levels but rarely at lower-levels. In this paper, we propose a framework that leverages both knowledge acquired from large RGB datasets together with depth-specific cues learned from the limited depth data, obtaining more effective multi-source and multi-modal representations. We propose a multi-modal combination method that selects discriminative combinations of layers from the different source models and target modalities, capturing both high-level properties of the task and intrinsic low-level properties of both modalities.  
  Address Melbourne; Australia; August 2017  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference IJCAI  
  Notes LAMP; 600.120 Approved no  
  Call Number Admin @ si @ SJH2017b Serial 2966  
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