  Deep Convolutional Neural Networks (CNNs) have demonstrated excellent
performance in image classification, but still show room for improvement in
object-detection tasks with many categories, in particular for cluttered scenes
and occlusion. Modern detection algorithms like Regions with CNNs (Girshick et
al., 2014) rely on Selective Search (Uijlings et al., 2013) to propose regions
which with high probability represent objects, where in turn CNNs are deployed
for classification. Selective Search represents a family of sophisticated
algorithms that are engineered with multiple segmentation, appearance and
saliency cues, typically coming with a significant run-time overhead.
Furthermore, (Hosang et al., 2014) have shown that most methods suffer from low
reproducibility due to unstable superpixels, even for slight image
perturbations. Although CNNs are subsequently used for classification in
top-performing object-detection pipelines, current proposal methods are
agnostic to how these models parse objects and their rich learned
representations. As a result they may propose regions which may not resemble
high-level objects or totally miss some of them. To overcome these drawbacks we
propose a boosting approach which directly takes advantage of hierarchical CNN
features for detecting regions of interest fast. We demonstrate its performance
on ImageNet 2013 detection benchmark and compare it with state-of-the-art
methods.
