Toward Joint Thing-and-Stuff Mining for Weakly Supervised Panoptic Segmentation-Reference-Cited by-同舟云学术

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Toward Joint Thing-and-Stuff Mining for Weakly Supervised Panoptic Segmentation

Published:2021-06 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

Shen Yunhang,Cao Liujuan,Chen Zhiwei,Lian Feihong,Zhang Baochang,Su Chi,Wu Yongjian,Huang Feiyue,Ji Rongrong

Funder

National Science Fund for Distinguished Young Scholars

National Natural Science Foundation of China

Nova

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9577055/9577056/09577537.pdf?arnumber=9577537

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Mask-Pyramid Network: A Novel Panoptic Segmentation Method;Sensors;2024-02-22

2. Point2Mask: Point-supervised Panoptic Segmentation via Optimal Transport;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

3. A Survey on Label-Efficient Deep Image Segmentation: Bridging the Gap Between Weak Supervision and Dense Prediction;IEEE Transactions on Pattern Analysis and Machine Intelligence;2023-08

4. Annotating Only at Definite Pixels: A Novel Weakly Supervised Semantic Segmentation Method for Sea Fog Recognition;2022 IEEE International Conference on Visual Communications and Image Processing (VCIP);2022-12-13

5. A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D Images;Applied Artificial Intelligence;2022-02-09

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