Panoptic segmentation unifies the typically distinct tasks of semantic segmentation (assign a class label to each pixel) and instance segmentation (detect and segment each object instance).
( Image credit: Detectron2 )
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In this paper, we explore this mechanism in the backbone design for object detection.
SOTA for Object Detection on COCO test-dev
While image classification models have recently continued to advance, most downstream applications such as object detection and semantic segmentation still employ ResNet variants as the backbone network due to their simple and modular structure.
SOTA for Semantic Segmentation on ADE20K
In this technical report, we present two novel datasets for image scene understanding.
Importantly, we take one step further by dynamically learning the mask head of the object segmenter such that the mask head is conditioned on the location.
#6 best model for Instance Segmentation on COCO test-dev
In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve comparable performance of two-stage methods while yielding fast inference speed.
#2 best model for Panoptic Segmentation on Cityscapes test (using extra training data)
In order to overcome the lack of supervision, we introduce a differentiable module to resolve the overlap between any pair of instances.
#2 best model for Panoptic Segmentation on COCO test-dev
We hope that CenterMask and VoVNetV2 can serve as a solid baseline of real-time instance segmentation and backbone network for various vision tasks, respectively.
#4 best model for Instance Segmentation on COCO minival