FSG-Net: a Deep Learning model for Semantic Robot Grasping through Few-Shot Learning-Reference-Cited by-同舟云学术

FSG-Net: a Deep Learning model for Semantic Robot Grasping through Few-Shot Learning

Published:2023-05-29 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Robotics and Automation (ICRA)
language:
Short-container-title:

Author:

Barcellona Leonardo¹,Bacchin Alberto¹,Gottardi Alberto¹,Menegatti Emanuele¹,Ghidoni Stefano¹

Affiliation:

1. University of Padova,Intelligent Autonomous System Lab,Department of Information Engineering,Padua,Italy,35131

Funder

MIUR

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10160211/10160212/10160618.pdf?arnumber=10160618

Reference36 articles.

1. Fully convolutional networks for semantic segmentation;long;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2015

2. Hypercorrelation squeeze for few-shot segmentation;min;Proceedings of the IEEE/CVF International Conference on Computer Vision,2021

3. Adaptive Prototype Learning and Allocation for Few-Shot Segmentation

4. Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?

5. U-net: Convolutional networks for biomedical image segmentation;ronneberger;International Confer-ence on Medical image computing and computer-assisted intervention,2015

Cited by 2 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. Multimodal Attention-Based Instruction-Following Part-Level Affordance Grounding;Applied Sciences;2024-05-29

2. Uncertainty-driven Exploration Strategies for Online Grasp Learning;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13