  In this paper we evaluate the quality of the activation layers of a
convolutional neural network (CNN) for the gen- eration of object proposals. We
generate hypotheses in a sliding-window fashion over different activation
layers and show that the final convolutional layers can find the object of
interest with high recall but poor localization due to the coarseness of the
feature maps. Instead, the first layers of the network can better localize the
object of interest but with a reduced recall. Based on this observation we
design a method for proposing object locations that is based on CNN features
and that combines the best of both worlds. We build an inverse cascade that,
going from the final to the initial convolutional layers of the CNN, selects
the most promising object locations and refines their boxes in a coarse-to-fine
manner. The method is efficient, because i) it uses the same features extracted
for detection, ii) it aggregates features using integral images, and iii) it
avoids a dense evaluation of the proposals due to the inverse coarse-to-fine
cascade. The method is also accurate; it outperforms most of the previously
proposed object proposals approaches and when plugged into a CNN-based detector
produces state-of-the- art detection performance.
