  We propose a Convolutional Neural Network (CNN) based algorithm - StuffNet -
for object detection. In addition to the standard convolutional features
trained for region proposal and object detection [31], StuffNet uses
convolutional features trained for segmentation of objects and 'stuff'
(amorphous categories such as ground and water). Through experiments on Pascal
VOC 2010, we show the importance of features learnt from stuff segmentation for
improving object detection performance. StuffNet improves performance from
18.8% mAP to 23.9% mAP for small objects. We also devise a method to train
StuffNet on datasets that do not have stuff segmentation labels. Through
experiments on Pascal VOC 2007 and 2012, we demonstrate the effectiveness of
this method and show that StuffNet also significantly improves object detection
performance on such datasets.
