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A Unified Neural Network for Panoptic Segmentation
Author(s) -
Yao L.,
Chyau A.
Publication year - 2019
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13852
Subject(s) - computer science , segmentation , pyramid (geometry) , artificial intelligence , metric (unit) , feature (linguistics) , artificial neural network , task (project management) , deep neural networks , pattern recognition (psychology) , market segmentation , computer vision , operations management , linguistics , physics , philosophy , business , management , marketing , optics , economics
In this paper, we propose a unified neural network for panoptic segmentation, a task aiming to achieve more fine‐grained segmentation. Following existing methods combining semantic and instance segmentation, our method relies on a triple‐branch neural network for tackling the unifying work. In the first stage, we adopt a ResNet50 with a feature pyramid network (FPN) as shared backbone to extract features. Then each branch leverages the shared feature maps and serves as the stuff, things, or mask branch. Lastly, the outputs are fused following a well‐designed strategy. Extensive experimental results on MS‐COCO dataset demonstrate that our approach achieves a competitive Panoptic Quality (PQ) metric score with the state of the art.