Attention-Guided Unified Network for Panoptic Segmentation
| Autor: | Xinze Chen, Yanwei Li, Dalong Du, Guan Huang, Zheng Zhu, Xingang Wang, Lingxi Xie |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Deep learning Computer Science - Computer Vision and Pattern Recognition Pattern recognition 02 engineering and technology Image segmentation 010501 environmental sciences 01 natural sciences Task (project management) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | CVPR |
| Popis: | This paper studies panoptic segmentation, a recently proposed task which segments foreground (FG) objects at the instance level as well as background (BG) contents at the semantic level. Existing methods mostly dealt with these two problems separately, but in this paper, we reveal the underlying relationship between them, in particular, FG objects provide complementary cues to assist BG understanding. Our approach, named the Attention-guided Unified Network (AUNet), is a unified framework with two branches for FG and BG segmentation simultaneously. Two sources of attentions are added to the BG branch, namely, RPN and FG segmentation mask to provide object-level and pixel-level attentions, respectively. Our approach is generalized to different backbones with consistent accuracy gain in both FG and BG segmentation, and also sets new state-of-the-arts both in the MS-COCO (46.5% PQ) and Cityscapes (59.0% PQ) benchmarks. CVPR 2019 |
| Databáze: | OpenAIRE |
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