Title: Contextual Image Parsing via Panoptic Segment Sorting. Abstract: Visual context is versatile and hard to describe or label precisely.  We aim to leverage the densely labeled task, image parsing, a.k.a panoptic segmentation, to learn a model that encodes and discovers object-centric context.  Most existing approaches based on deep learning tackle image parsing via fusion of pixel-wise classification and instance masks from two sub-networks.  Such approaches isolate things from stuff and fuse the semantic and instance masks in the later stage.   To encode object-centric context inherently, we propose a metric learning framework, Panoptic Segment Sorting, that is directly trained with stuff and things jointly.  Our key insight is to make the panoptic embeddings separate every instance so that the model automatically learns to leverage visual context as many instances across different images appear similar.  We show that the context of our model's retrieved instances is more consistent relatively by 13.7%, further demonstrating its ability to discover novel context unsupervisedly.  Our overall framework also achieves competitive performance across standard panoptic segmentation metrics amongst the state-of-the-art methods on two large datasets, Cityscapes and PASCAL VOC.  These promising results suggest that pixel-wise embeddings can not only inject new understanding into panoptic segmentation but potentially serve for other tasks such as modeling instance relationships.