Unseen Object Few-Shot Semantic Segmentation for Robotic Grasping-Reference-Cited by-同舟云学术

Unseen Object Few-Shot Semantic Segmentation for Robotic Grasping

Published:2023-01 Issue:1 Volume:8 Page:320-327
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Liu Xiaozheng¹^ORCID,Zhang Yunzhou¹^ORCID,Shan Dexing¹^ORCID

Affiliation:

1. College of Information Science and Engineering, Northeastern University, Shenyang, China

Funder

National Natural Science Foundation of China

Major Science and Technology Projects of Liaoning Province

Foundation of Key Laboratory of Aerospace System Simulation

Foundation of Key Laboratory of Equipment Reliability

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/9963768/09968085.pdf?arnumber=9968085

Reference40 articles.

1. RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks

2. Understanding the effective receptive field in deep convolutional neural networks;luo;Proc Adv Neural Inf Process Syst,0

3. AirDet: Few-Shot Detection Without Fine-Tuning for Autonomous Exploration

4. CCAN: Constraint Co-Attention Network for Instance Grasping

5. A Multi-task Learning Framework for Grasping-Position Detection and Few-Shot Classification

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