We are interested in how semantic segmentation can help object detection. We propose a novel deformable part-based model which exploits segmentation algorithms that compute candidate object regions. Our approach allows every detection hypothesis to select a segment, and scores each box in the image using both the traditional HOG filters as well as a set of novel segmentation features. Thus our model ``blends'' between the detector and segmentation models. Our approach significantly outperforms DPM and existing state-of-the-art approaches on the challenging PASCAL VOC 2010 dataset.