GaussianMask: Uncertainty-aware Instance Segmentation based on Gaussian Modeling-Reference-Cited by-同舟云学术

GaussianMask: Uncertainty-aware Instance Segmentation based on Gaussian Modeling

Published:2022-08-21 Issue: Volume: Page:
ISSN:
Container-title:2022 26th International Conference on Pattern Recognition (ICPR)
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Author:

Lee Seung Il¹,Kim Hyun¹

Affiliation:

1. Seoul National University of Science and Technology,Research Center for Electrical and Information Technology,Department of Electrical and Information Engineering,Seoul,Korea

Funder

National Research Foundation of Korea

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9956007/9955631/09956515.pdf?arnumber=9956515

Reference41 articles.

1. Retinamask: Learning to predict masks improves state-of-the-art single-shot detection for free;fu,2019

2. Instant and accurate instance segmentation equipped with path aggregation and attention gate;lee;International SoC Design Conference (ISOCC),2020

3. On calibration of modern neural networks;guo;Int Conference on Machine Learning,2017

4. Fully convolutional networks for semantic segmentation

5. MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features

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