<> "The repository administrator has not yet configured an RDF license."^^ . <> . . "Exploring Aspects of Image Segmentation: Diversity, Global Reasoning, and Panoptic Formulation"^^ . "Image segmentation is the task of partitioning an image intomeaningful regions. It is a fundamental part of the visual scene understanding problem with many real-world applications, such as photo-editing, robotics, navigation, autonomous driving and bio-imaging. It has been extensively studied for several decades and has transformed into a set of problems which define meaningfulness of regions differently. The set includes two high-level tasks: semantic segmentation (each region assigned with a semantic label) and instance segmentation (each region representing object instance). Due to their practical importance, both tasks attract a lot of research attention. In this work we explore several aspects of these tasks and propose novel approaches and new paradigms.\r\n\r\nWhile most research efforts are directed at developing models that produce a single best segmentation, we consider the task of producing multiple diverse solutions given a single input image. This allows to hedge against the intrinsic ambiguity of segmentation task. We propose a new global model with multiple solutions for a trained segmentation model. This new model generalizes previously proposed approaches for the task. We present several approximate and exact inference techniques that suit a wide spectrum of possible applications and demonstrate superior performance comparing to previous methods.\r\n\r\nThen, we present a new bottom-up paradigm for the instance segmentation task. The new scheme is substantially different from the previous approaches that produce each instance independently. Our approach named InstanceCut reasons globally about the optimal partitioning of an image into instances based on local clues. We use two types of local pixel-level clues extracted by efficient fully convolutional networks: (i) an instance-agnostic semantic segmentation and (ii) instance boundaries. Despite the conceptual simplicity of our approach, it demonstrates promising performance.\r\n\r\nFinally, we put forward a novel Panoptic Segmentation task. It unifies semantic and instance segmentation tasks. The proposed task requires generating a coherent scene segmentation that is rich and complete, an important step towards real-world vision systems. While early work in computer vision addressed related image/scene parsing tasks, these are not currently popular, possibly due to lack of appropriate metrics or associated recognition challenges. To address this, we first offer a novel panoptic quality metric that captures performance for all classes (stuff and things) in an interpretable and unified manner. Using this metric, we perform a rigorous study of both human and machine performance for panoptic segmentation on three existing datasets, revealing interesting insights about the task. The aim of our work is to revive the interest of the community in a more unified view of image segmentation."^^ . "2018" . . . . . . . "Akirillov"^^ . "Kirillov"^^ . "Akirillov Kirillov"^^ . . . . . . "Exploring Aspects of Image Segmentation: Diversity, Global Reasoning, and Panoptic Formulation (PDF)"^^ . . . "thesis_kirillov.pdf"^^ . . . "Exploring Aspects of Image Segmentation: Diversity, Global Reasoning, and Panoptic Formulation (Other)"^^ . . . . . . "indexcodes.txt"^^ . . . "Exploring Aspects of Image Segmentation: Diversity, Global Reasoning, and Panoptic Formulation (Other)"^^ . . . . . . "lightbox.jpg"^^ . . . "Exploring Aspects of Image Segmentation: Diversity, Global Reasoning, and Panoptic Formulation (Other)"^^ . . . . . . "preview.jpg"^^ . . . "Exploring Aspects of Image Segmentation: Diversity, Global Reasoning, and Panoptic Formulation (Other)"^^ . . . . . . "medium.jpg"^^ . . . "Exploring Aspects of Image Segmentation: Diversity, Global Reasoning, and Panoptic Formulation (Other)"^^ . . . . . . "small.jpg"^^ . . "HTML Summary of #25750 \n\nExploring Aspects of Image Segmentation: Diversity, Global Reasoning, and Panoptic Formulation\n\n" . "text/html" . . . "004 Informatik"@de . "004 Data processing Computer science"@en . .