learn aspect-specific pixel-level segmentation masks for different categories. These are readily learned by averaging together segmentation masks from aligned training instances. We learn different masks corresponding to the different mixtures in our DPM detector. Sample masks are visualized in Fig. \[fig:masks\].

![We visualize our mixture-specific shape masks. We paste thresholded shape masks on each candidate detection to generate candidate segments.\[fig:masks\]](figs/shape_prior){width="\columnwidth"}

![Evaluating instance detections with segmentation masks versus bounding boxes. Bounding boxes are a particularly crude approximation for articulated objects; in this case, the majority of the pixels in the ([**blue**]{}) tight-fitting bounding-box do not lie on the object. Our ([**green**]{}) instance-level segmentation masks allows for a more accurate measure of object detection and localization.\[fig:segment\]](figs/segment){width="\columnwidth"}

[**Detection evaluated by segmentation** ]{} Segmentation is a challenging task even assuming a detector reports correct results as it requires fine localization of object part boundaries. To decouple segmentation evaluation from detection correctness, we benchmark segmentation quality using only correct detections. Specifically, given that the detector reports a correct bounding box, how well does the predicted segmentation of that object match the ground truth segmentation? As criterion for correct detection, we impose the standard requirement that intersection over union between predicted and ground truth boxes is 