In this work, we introduce the new scene understanding task of Part-aware Panoptic Segmentation (PPS), which aims to understand a scene at multiple levels of abstraction, and unifies the tasks of scene parsing and part parsing. For this novel task, we provide consistent annotations on two commonly used datasets: Cityscapes and Pascal VOC. Moreover, we present a single metric to evaluate PPS, called Part-aware Panoptic Quality (PartPQ). For this new task, using the metric and annotations, we set multiple baselines by merging results of existing state-of-the-art methods for panoptic segmentation and part segmentation. Finally, we conduct several experiments that evaluate the importance of the different levels of abstraction in this single tas...
It is natural to represent objects in terms of their parts. This has the potential to improve the pe...
Pixel-wise semantic segmentation is capable of unifying most of driving scene perception tasks, and ...
Panoptic segmentation combines instance and semantic predictions, allowing the detection of "things"...
In this work, we introduce the new scene understanding task of Part-aware Panoptic Segmentation (PPS...
In this work, we present two novel datasets for image scene understanding. Both datasets have annota...
Panoptic segmentation is a recently proposed task that unifies both instance and semantic segmentati...
Image segmentation is the task of partitioning an image intomeaningful regions. It is a fundamental ...
In this work, we introduce panoramic panoptic segmentation, as the most holistic scene understanding...
We present a single network method for panoptic segmentation. This method combines the predictions f...
We present an end-to-end network to bridge the gap between training and inference pipeline for panop...
Full visual scene understanding has always been one of the main goals of machine perception. The abi...
\u3cp\u3eIn this work, we propose a single deep neural network for panoptic segmentation, for which ...
In this work, we present an end-to-end network for fast panoptic segmentation. This network, called ...
We present a weakly supervised model that jointly performs both semantic- and instance-segmentation ...
Panoptic segmentation provides a rich 2D environment representation by unifying semantic and instanc...
It is natural to represent objects in terms of their parts. This has the potential to improve the pe...
Pixel-wise semantic segmentation is capable of unifying most of driving scene perception tasks, and ...
Panoptic segmentation combines instance and semantic predictions, allowing the detection of "things"...
In this work, we introduce the new scene understanding task of Part-aware Panoptic Segmentation (PPS...
In this work, we present two novel datasets for image scene understanding. Both datasets have annota...
Panoptic segmentation is a recently proposed task that unifies both instance and semantic segmentati...
Image segmentation is the task of partitioning an image intomeaningful regions. It is a fundamental ...
In this work, we introduce panoramic panoptic segmentation, as the most holistic scene understanding...
We present a single network method for panoptic segmentation. This method combines the predictions f...
We present an end-to-end network to bridge the gap between training and inference pipeline for panop...
Full visual scene understanding has always been one of the main goals of machine perception. The abi...
\u3cp\u3eIn this work, we propose a single deep neural network for panoptic segmentation, for which ...
In this work, we present an end-to-end network for fast panoptic segmentation. This network, called ...
We present a weakly supervised model that jointly performs both semantic- and instance-segmentation ...
Panoptic segmentation provides a rich 2D environment representation by unifying semantic and instanc...
It is natural to represent objects in terms of their parts. This has the potential to improve the pe...
Pixel-wise semantic segmentation is capable of unifying most of driving scene perception tasks, and ...
Panoptic segmentation combines instance and semantic predictions, allowing the detection of "things"...