  Object detection often suffers from a plenty of bootless proposals, selecting
high quality proposals remains a great challenge. In this paper, we propose a
semantic, class-specific approach to re-rank object proposals, which can
consistently improve the recall performance even with less proposals. We first
extract features for each proposal including semantic segmentation, stereo
information, contextual information, CNN-based objectness and low-level cue,
and then score them using class-specific weights learnt by Structured SVM. The
advantages of the proposed model are twofold: 1) it can be easily merged to
existing generators with few computational costs, and 2) it can achieve high
recall rate uner strict critical even using less proposals. Experimental
evaluation on the KITTI benchmark demonstrates that our approach significantly
improves existing popular generators on recall performance. Moreover, in the
experiment conducted for object detection, even with 1,500 proposals, our
approach can still have higher average precision (AP) than baselines with 5,000
proposals.
