Object Detectors Emerge in Deep Scene CNNs

With the success of new computational architectures for visual processing, such as convolutional neural networks (CNN) and access to image databases with millions of labeled examples (e.g., ImageNet, Places), the state of the art in computer vision is advancing rapidly. One important factor for continued progress is to understand the representations that are learned by the inner layers of these deep architectures. Here we show that object detectors emerge from training CNNs to perform scene classification. As scenes are composed of objects, the CNN for scene classification automatically discovers meaningful objects detectors, representative of the learned scene categories. With object detectors emerging as a…

• NS
  National Science Foundation

  Award: 1016862
• N
  Nvidia
• MU
  Multidisciplinary University Research Initiative

  Award: N000141010933
• OO
  Office of Naval Research

  Awards: MURI N000141010933, N000141010933