Continual Learning for LiDAR Semantic Segmentation: Class-Incremental and Coarse-to-Fine strategies on Sparse Data-Reference-Cited by-同舟云学术

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Continual Learning for LiDAR Semantic Segmentation: Class-Incremental and Coarse-to-Fine strategies on Sparse Data

Published:2023-06 Issue: Volume: Page:
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Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Author:

Camuffo Elena¹,Milani Simone¹

Affiliation:

1. University of Padova,Department of Information Engineering,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10208270/10208119/10208892.pdf?arnumber=10208892

Reference53 articles.

1. PLOP: Learning without Forgetting for Continual Semantic Segmentation

2. I3dol: Incremental 3d object learning without catastrophic forgetting;dong;AAAI Conference on Artificial Intelligence,2020

3. Are we hungry for 3d lidar data for semantic segmentation? a survey of datasets and methods;gao;IEEE Transactions on Intelligent Transportation Systems,2021

4. PSTNet: Point spatio-temporal convolution on point cloud sequences;fan;ICLRE,2021

5. Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation

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1. Managing the unknown in machine learning: Definitions, related areas, recent advances, and prospects;Neurocomputing;2024-09

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