Conference paper Open Access

Learning Short-Cut Connections for Object Counting

Daniel Oñoro-Rubio; Mathias Niepert; Roberto J. López-Sastre


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  <dc:creator>Daniel Oñoro-Rubio</dc:creator>
  <dc:creator>Mathias Niepert</dc:creator>
  <dc:creator>Roberto J. López-Sastre</dc:creator>
  <dc:date>2018-11-15</dc:date>
  <dc:description>Object counting is an important task in computer vision due to its growing demand in applications such as traffic monitoring or surveillance. In this paper, we consider object counting as a learning problem of a joint feature extraction and pixel-wise object density estimation with Convolutional-Deconvolutional networks. We introduce a novel counting model, named Gated U-Net (GU-Net). Specifically, we propose to enrich the U-Net architecture with the concept of learnable short-cut connections. Standard short-cut connections are connections between layers in deep neural networks which skip at least one intermediate layer. Instead of simply setting short-cut connections, we propose to learn these connections from data. Therefore, our short-cuts can work as gating units, which optimize the flow of information between convolutional and deconvolutional layers in the U-Net architecture. We evaluate the introduced GU-Net architecture on three commonly used benchmark data sets for object counting. GU-Nets consistently outperform the base U-Net architecture, and achieve state-of-the-art performance.</dc:description>
  <dc:identifier>https://zenodo.org/record/3813490</dc:identifier>
  <dc:identifier>10.5281/zenodo.3813490</dc:identifier>
  <dc:identifier>oai:zenodo.org:3813490</dc:identifier>
  <dc:language>akh</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/761508/</dc:relation>
  <dc:relation>doi:10.5281/zenodo.3813489</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/5gcity</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:title>Learning Short-Cut Connections for Object Counting</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
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