TY - GEN
T1 - Fast Single Shot Instance Segmentation
AU - Li, Zuoxin
AU - Zhou, Fuqiang
AU - Yang, Lu
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - In this work, we propose fast single shot instance segmentation framework (FSSI), which aims at jointly object detection, segmenting and distinguishing every individual instance (instance segmentation) in a flexible and fast way. In the pipeline of FSSI, the instance segmentation task is divided into three parallel sub-tasks: object detection, semantic segmentation, and direction prediction. The instance segmentation result is then generated from these three sub-tasks’ results by the post-process in parallel. In order to accelerate the process, the SSD-like detection structure and two-path architecture which can generate more accurate segmentation prediction without heavy calculation burden are adopted. Our experiments on the PASCAL VOC and the MSCOCO datasets demonstrate the benefits of our approach, which accelerate the instance segmentation process with competitive result compared to MaskRCNN. Code is public available (https://github.com/lzx1413/FSSI).
AB - In this work, we propose fast single shot instance segmentation framework (FSSI), which aims at jointly object detection, segmenting and distinguishing every individual instance (instance segmentation) in a flexible and fast way. In the pipeline of FSSI, the instance segmentation task is divided into three parallel sub-tasks: object detection, semantic segmentation, and direction prediction. The instance segmentation result is then generated from these three sub-tasks’ results by the post-process in parallel. In order to accelerate the process, the SSD-like detection structure and two-path architecture which can generate more accurate segmentation prediction without heavy calculation burden are adopted. Our experiments on the PASCAL VOC and the MSCOCO datasets demonstrate the benefits of our approach, which accelerate the instance segmentation process with competitive result compared to MaskRCNN. Code is public available (https://github.com/lzx1413/FSSI).
KW - Convolutional Neural Networks
KW - Instance segmentation
KW - Multi-task learning
UR - https://www.scopus.com/pages/publications/85066866687
U2 - 10.1007/978-3-030-20870-7_16
DO - 10.1007/978-3-030-20870-7_16
M3 - 会议稿件
AN - SCOPUS:85066866687
SN - 9783030208691
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 257
EP - 272
BT - Computer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers
A2 - Mori, Greg
A2 - Schindler, Konrad
A2 - Li, Hongdong
A2 - Jawahar, C.V.
PB - Springer Verlag
T2 - 14th Asian Conference on Computer Vision, ACCV 2018
Y2 - 2 December 2018 through 6 December 2018
ER -