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Article overview
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Learning Category- and Instance-Aware Pixel Embedding for Fast Panoptic Segmentation | Naiyu Gao
; Yanhu Shan
; Xin Zhao
; Kaiqi Huang
; | Date: |
28 Sep 2020 | Abstract: | Panoptic segmentation (PS) is a complex scene understanding task that
requires providing high-quality segmentation for both thing objects and stuff
regions. Previous methods handle these two classes with semantic and instance
segmentation modules separately, following with heuristic fusion or additional
modules to resolve the conflicts between the two outputs. This work simplifies
this pipeline of PS by consistently modeling the two classes with a novel PS
framework, which extends a detection model with an extra module to predict
category- and instance-aware pixel embedding (CIAE). CIAE is a novel pixel-wise
embedding feature that encodes both semantic-classification and
instance-distinction information. At the inference process, PS results are
simply derived by assigning each pixel to a detected instance or a stuff class
according to the learned embedding. Our method not only shows fast inference
speed but also the first one-stage method to achieve comparable performance to
two-stage methods on the challenging COCO benchmark. | Source: | arXiv, 2009.13342 | Services: | Forum | Review | PDF | Favorites |
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