RobotVQA — A Scene-Graph- and Deep-Learning-based Visual Question Answering System for Robot Manipulation-Reference-Cited by-同舟云学术

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RobotVQA — A Scene-Graph- and Deep-Learning-based Visual Question Answering System for Robot Manipulation

Published:2020-10-24 Issue: Volume: Page:
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Container-title:2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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Author:

Kenghagho Kenfack Franklin,Ahmed Siddiky Feroz,Balint-Benczedi Ferenc,Beetz Michael

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9340668/9340635/09341186.pdf?arnumber=9341186

Cited by 11 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. Perception through Cognitive Emulation : "A Second Iteration of NaivPhys4RP for Learningless and Safe Recognition and 6D-Pose Estimation of (Transparent) Objects";2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

2. A Generalizable Architecture for Explaining Robot Failures Using Behavior Trees and Large Language Models;Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction;2024-03-11

3. An Active Task Cognition Method for Home Service Robot Using Multi-Graph Attention Fusion Mechanism;IEEE Transactions on Circuits and Systems for Video Technology;2024

4. Design of knowledge incorporated VQA based on spatial GCNN with structured sentence embedding and linking algorithm;Journal of Intelligent & Fuzzy Systems;2023-12-02

5. Robust Construction of Spatial-Temporal Scene Graph Considering Perception Failures for Autonomous Driving;2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC);2023-09-24

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