  In CNN-based object detection methods, region proposal becomes a bottleneck
when objects exhibit significant scale variation, occlusion or truncation. In
addition, these methods mainly focus on 2D object detection and cannot estimate
detailed properties of objects. In this paper, we propose subcategory-aware
CNNs for object detection. We introduce a novel region proposal network that
uses subcategory information to guide the proposal generating process, and a
new detection network for joint detection and subcategory classification. By
using subcategories related to object pose, we achieve state-of-the-art
performance on both detection and pose estimation on commonly used benchmarks.
