no code implementations • 1 Mar 2024 • Wenqi Liang, Gan Sun, Qian He, Yu Ren, Jiahua Dong, Yang Cong
Relying on large language models (LLMs), embodied robots could perform complex multimodal robot manipulation tasks from visual observations with powerful generalization ability.
no code implementations • 22 Feb 2023 • Yu Ren, Guoli Wang, PingPing Wang, Kunmeng Liu, Quanjin Liu, Hongfu Sun, Xiang Li, Benzheng Wei
Conclusions: The experimental result demonstrates the effectiveness of the proposed MM-SFENet on the localization and classification of bladder cancer.
no code implementations • CVPR 2023 • Yu Ren, Ronghan Chen, Yang Cong
In comparison with most methods focusing on 3D rigid object recognition and manipulation, deformable objects are more common in our real life but attract less attention.
no code implementations • 28 Nov 2022 • Yu Ren, Xiaoling Zhang, Xu Zhan, Jun Shi, Shunjun Wei, Tianjiao Zeng
To address that, we propose a new model-data-driven network to achieve tomoSAR imaging based on multi-dimensional features.
no code implementations • 21 Sep 2022 • Yu Ren, Xiaoling Zhang, Yunqiao Hu, Xu Zhan
To address them, in this paper, a novel imaging network (AETomo-Net) based on multi-dimensional features is proposed.
no code implementations • 15 Jul 2021 • Danial Kamran, Yu Ren, Martin Lauer
Reinforcement learning (RL) has recently been used for solving challenging decision-making problems in the context of automated driving.