Distinct Class Saliency Maps for Multiple Object Images

Wataru Shimoda, Keiji Yanai

Feb 18, 2016 (modified: Feb 18, 2016) ICLR 2016 workshop submission readers: everyone
  • Abstract: This paper proposes a method to obtain more distinct class saliency maps than Simonyan et al. (2014). We made three improvements over their method: (1) using CNN derivatives with respect to feature maps of the intermediate convolutional layers with up-sampling instead of an input image; (2) subtracting saliency maps of the other classes from saliency maps of the target class to differentiate target objects from other objects; (3) aggregating multi-scale class saliency maps to compensate lower resolution of the feature maps.
  • Conflicts: uec.ac.jp

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