Incremental Few-Shot Object Detection for Robotics

Author:

Li Yiting1,Zhu Haiyue2,Tian Sichao1,Feng Fan1,Ma Jun3,Teo Chek Sing2,Xiang Cheng1,Vadakkepat Prahlad1,Lee Tong Heng1

Affiliation:

1. National University of Singapore,Singapore,117583

2. Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research,Adaptive Robotics and Mechatronics Group,Singapore,138634

3. The Hong Kong University of Science and Technology (Guangzhou),Robotics and Autonomous Systems Thrust,Guangzhou,China

Publisher

IEEE

Reference28 articles.

1. Grasping Detection Network with Uncertainty Estimation for Confidence-Driven Semi-Supervised Domain Adaptation

2. Weight imprinting classification-based force grasping with a variable-stiffness robotic gripper;zhu;IEEE Transactions on Automation Science and Engineering,2021

3. Frustratingly Simple Few-Shot Object Detection;wang;ArXiv e-prints,2020

4. BBN: Bilateral-Branch Network With Cumulative Learning for Long-Tailed Visual Recognition

5. Overcoming catastrophic forgetting by incremental moment matching;lee;ArXiv Preprint,2017

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1. Multi-Frequency-Aware Patch Adversarial Learning for Neural Point Cloud Rendering;2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM);2024-07-15

2. Few-shot object detection: Research advances and challenges;Information Fusion;2024-07

3. An Incremental Few Shot Approach for Litter Detection;Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning;2024-03-22

4. Leveraging Bilateral Correlations for Multi-Label Few-Shot Learning;IEEE Transactions on Neural Networks and Learning Systems;2024

5. Reinforced Self-Supervised Training for Few-Shot Learning;IEEE Signal Processing Letters;2024

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