are better.

Consistent with past observations [@zhu2012we], we find that including difficult (non-iconic) images during training may not always help. Such examples may act as noise and pollute the learned model if the model is not rich enough to capture such appearance variability. Our dataset allows for the exploration of such issues.

Torralba and Efros [@torralba2011unbiased] proposed a metric to measure cross-dataset generalization which computes the ‘performance drop’ for models that train on one dataset and test on another. The performance difference of the DPMv5-P models across the two datasets is 12.7 AP while the DPMv5-C models only have 7.7 AP difference. Moreover, overall performance is much lower on [MS COCO]{}. These observations support two hypotheses: 1) [MS COCO]{}is significantly more difficult than PASCAL VOC and 2) models trained on [MS COCO]{}can generalize better to easier datasets such as PASCAL VOC given more training data. To gain insight into the differences between the datasets, see [the appendix]{}for visualizations of person and chair examples from the two datasets.

[**Generating segmentations from detections** ]{} We now describe a simple method for generating object bounding boxes and segmentation masks, following prior work that produces segmentations from object detections [@brox2011object; @yang2012layered; @ramanan2007using; @dai2012learning]. We 