Contextual Image Parsing via Panoptic Segment Sorting

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 . 1 INTRODUCTION . Visual context is versatile and hard to describe or label precisely , yet it is critical for humans ( Medin & Schaffer , 1978 ) to recognize objects quickly . More importantly , objects in different contexts carry different meanings . For example , pedestrians walking in crosswalks should receive more attention than on sidewalks to a driver . However , it is almost impossible to categorize objects with different contexts as the change can be subtile yet dramatic : A pedestrian is more likely in danger if walking in front of a car than by a car , where both a person and a car appear together . We are thus motivated to propose a model that automatically encodes and discovers object visual context by leveraging a densely labeled task , panoptic segmentation . Panoptic segmentation ( Kirillov et al. , 2019b ) , a.k.a. , image parsing ( Tu et al. , 2005 ) , is to segment an image into its constituent visual patterns with both semantic and instance labels . The major challenge lies in delineating different instances while associating them with semantic categories . For example , one has to segment two side-by-side cars apart while still being able to classify them as the same category . Most existing approaches ( Kirillov et al. , 2019a ; Xiong et al. , 2019 ; Yang et al. , 2019 ; Cheng et al. , 2020 ; Li et al. , 2020 ) tackle these two aspects via two sub-networks , instance and semantic segmentation branches . The advantage of such approaches is that each branch can cater to one aspect and achieves high performance . Additional modules for integrating things and stuff are needed to resolve the disagreements between two branches . Yet for object visual context , things and stuff are two integral parts . Hence , we aim to propose a framework that unifies these two seemingly competing aspects and thus encodes visual context inherently . Our framework is inspired by the perceptual organization view ( Biederman , 1987 ) : Humans perceive a scene by breaking it down into visual groups and structures ; repeated structures are then associated for cognitive recognition . Our key insight is to separate everything first and group visually similar components later . The grouping takes place within an image and across images . Within an image , visually similar segments are merged to form instances ; across images , visually familiar segments are associated to create semantics , as illustrated in Fig . 1 . We carry out this idea by building an end-to-end trained pixel-wise embedding framework . Each pixel in an image is mapped via a CNN to a feature in latent space , and nearby features indicate pixels belonging to the same instance . This framework is therefore a non-parametric model at the segment and instance levels as its complexity scales with number of segments and instances , i.e. , exemplars . Particularly , by forcing all the instances to separate , the model has to utilize all the possible visual and semantic information . The model thus learns to separate instances by not only their appearances but also their surroundings , or visual context . A major difference between our model and others is the metric learning perspective : Our model trains with a contrastive loss that captures pixelto-segment relationships while others trains with pixel-wise classification that predicts category or instance directly . As a result , the learned panoptic embeddings can discover instances under similar context , as in Figure 1 bottom row . Specifically , we adapt the Segment Sorting approach ( Hwang et al. , 2019b ) to panoptic segmentation by sorting segments according to both of its semantic and instance labels , hence dubbed Panoptic Segment Sorting ( PSS ) . Such trained pixel-wise embeddings thus encode both semantic and instance information . We then predict each segment ’ s semantic label by simply mapping and classifying its prototype feature with a softmax classifier . We also propose a corresponding clustering algorithm to merge segments into instances with a nearest neighbor criterion ( Sarfraz et al. , 2019 ) . To alleviate the problem of instances with various scales , we further equip our framework with hybrid scale exemplars during training and dynamic partitioning during inference . Finally , we facilitate the merging process with a seeding branch that predicts the center of each instance . As a result , we demonstrate that the contexts of instances retrieved by our panoptic embeddings are more consistent relatively by 13.7 % while achieving competitive performance amongst the stateof-the-art on two datasets , Cityscapes ( Cordts et al. , 2016 ) and PASCAL VOC ( Everingham et al. ) . These promising results suggest that Panoptic Segment Sorting or pixel-wise embeddings can not only inject new understanding into panoptic segmentation but potentially serve as a foundation for other tasks such as discovering novel contexts or modeling instance relationships . 2 RELATED WORK . Image parsing and panoptic segmentation . The task of image parsing is first introduced in Tu et al . ( 2005 ) , where they formulate the solution in a Bayesian framework and construct a parsing graph as output . Since then , a lot of work has attempted to solve holistic scene understanding ( Zhu & Mumford ( 2007 ) ; Malisiewicz & Efros ( 2008 ) ; Tighe & Lazebnik ( 2013 ) ; Rabinovich et al . ( 2007 ) ; Yao et al . ( 2012 ) ) . Recently , Kirillov et al . ( 2019b ) reintroduce image parsing in the context of deep learning with large-scale datasets and new evaluation metric , renaming the task as panoptic segmentation as to unify the well-developed semantic and instance segmentation . Many research efforts ( Li et al. , 2018b ; Kirillov et al. , 2019a ; Xiong et al. , 2019 ; Porzi et al. , 2019 ; Yang et al. , 2019 ; Liu et al. , 2019 ; Li et al. , 2019 ; 2018a ; Gao et al. , 2019 ; Chen et al. , 2020 ; Wu et al. , 2020 ; Wang et al. , 2020 ; Li et al. , 2020 ) have followed quickly . The common approaches embrace the concept of unifying instance and semantic segmentation by integrating the time-tested object proposal and segmentation framework popularized by Mask R-CNN ( He et al. , 2017 ) . Instance segmentation . This task is generally approached by two camps of solutions : top-down or bottom-up . The top-down approaches ( Dai et al. , 2016 ; Li et al. , 2017 ; Dai et al. , 2017 ; He et al. , 2017 ; Chen et al. , 2018a ; Liu et al. , 2018a ) adopt a two-stage framework where the bounding boxes are proposed by a detection network ( Ren et al. , 2015 ) and the segmentation masks are produced by an add-on head . The bottom-up approaches ( Carreira & Sminchisescu , 2011 ; Arbeláez et al. , 2014 ; Pinheiro et al. , 2015 ; 2016 ; Bai & Urtasun , 2017 ; Liu et al. , 2017a ; Kirillov et al. , 2017 ; Newell et al. , 2017 ; Fathi et al. , 2017 ; Kendall et al. , 2018 ; Liu et al. , 2018b ; Papandreou et al. , 2018 ; Zhou et al. , 2019 ) predict and encode pair-wise relationships in various forms and segment the instance accordingly . Instance context . Instance contexts and relationships are explored mainly to enhance the detection performance . Earlier work ( Malisiewicz & Efros , 2009 ) models the appearances and 2D spatial context as a graph . Recently , researchers integrate graphs ( Chen et al. , 2018c ) or spatial memory ( Chen & Gupta , 2017 ) into the deep learning framework . The distinction of our work is that our model does not explicitly model contexts yet is able to discovers novel contexts automatically . Semantic segmentation . Current state-of-the-art semantic segmentation approaches develop from fully convolutional networks ( Long et al. , 2015 ; Chen et al. , 2016 ) , with various innovations . Incorporating contextual information ( Ronneberger et al. , 2015 ; Yu & Koltun , 2016 ; Xie et al. , 2016 ; Zhao et al. , 2017 ; Chen et al. , 2017 ; 2018b ) , and encoding pair-wise relationships ( Zheng et al. , 2015 ; Bertasius et al. , 2016 ; Liu et al. , 2017b ; Bertasius et al. , 2017 ; Maire et al. , 2016 ; Mostajabi et al. , 2018 ; Kong & Fowlkes , 2018 ; Ke et al. , 2018 ; Hwang et al. , 2019a ; b ) are the two major research lines . Non-parametric segmentation . Prior to deep learning ’ s emergence , non-parametric models ( Russell et al. , 2009 ; Tighe & Lazebnik , 2010 ; Liu et al. , 2011 ) usually use hand-craft features with statistical models or graphical models to segment images with pixel-wise labels . Deep metric learning methods ( Fathi et al. , 2017 ; Neven et al. , 2019 ) for instance segmentation emphasize the simplicity and fast computation . More recently , inspired by non-parametric models ( Wu et al. , 2018b ; a ) for image recognition , SegSort ( Hwang et al. , 2019b ) , upon which our work is built , captures pixel-tosegment relationships via pixel-wise embeddings , proposing the first deep non-parametric semantic segmentation in both supervised and unsupervised settings . 3 METHOD . Our end-to-end framework consists of a major SegSort branch and a seeding branch , both of which share one backbone network that generates multi-scale pixel-wise features . The SegSort branch outputs pixel-wise panoptic embeddings , which encode both semantic and instance information and are thus used to discover instance-centric context . The over-segmentations induced by the embeddings are then merged into instances and segments are classified by a softmax classifier . The seeding branch predicts the center of instances , which guide the merging process to reduce false positives . The overall framework is illustrated in Figure 2 . This section is organized as follows . We first briefly review the Segment Sorting framework for semantic segmentation in Sec . 3.1 . We then describe how to extend it for panoptic segmentation in Sec . 3.2 . In Sec . 3.3 , we further develop a dynamic partitioning mechanism to alleviate the problem of varying scales of instances . Finally , we briefly describe the seeding branch in Sec . 3.4 that helps decide the ownership of boundaries .

Summary: The paper presents a pixel-wise embedding strategy for panoptic segmentation, which aims to learn a pixel representation that encodes both semantic and instance information. To this end, the proposed method builds on top of the Segment Sorting approach and extends its contrastive loss to the instance level by utilizing panoptic supervision. To predict instance segmentation,  the paper also designs a merging process to cluster the pixels into instances, which further employs an object center prediction module for localization and a dynamic partition strategy to cope with scale variation.  This method is evaluated on two panotpic segmentation benchmarks, including Cityscapes and PASCAL VOC 2012.