no code implementations • 28 Mar 2024 • Zeren Chen, Zhelun Shi, Xiaoya Lu, Lehan He, Sucheng Qian, Hao Shu Fang, Zhenfei Yin, Wanli Ouyang, Jing Shao, Yu Qiao, Cewu Lu, Lu Sheng
The ultimate goals of robotic learning is to acquire a comprehensive and generalizable robotic system capable of performing both seen skills within the training distribution and unseen skills in novel environments.
1 code implementation • 26 Mar 2024 • Zhelun Shi, Zhipin Wang, Hongxing Fan, Zaibin Zhang, Lijun Li, Yongting Zhang, Zhenfei Yin, Lu Sheng, Yu Qiao, Jing Shao
Large Language Models (LLMs) aim to serve as versatile assistants aligned with human values, as defined by the principles of being helpful, honest, and harmless (hhh).
no code implementations • 26 Jan 2024 • Chaochao Lu, Chen Qian, Guodong Zheng, Hongxing Fan, Hongzhi Gao, Jie Zhang, Jing Shao, Jingyi Deng, Jinlan Fu, Kexin Huang, Kunchang Li, Lijun Li, LiMin Wang, Lu Sheng, Meiqi Chen, Ming Zhang, Qibing Ren, Sirui Chen, Tao Gui, Wanli Ouyang, Yali Wang, Yan Teng, Yaru Wang, Yi Wang, Yinan He, Yingchun Wang, Yixu Wang, Yongting Zhang, Yu Qiao, Yujiong Shen, Yurong Mou, Yuxi Chen, Zaibin Zhang, Zhelun Shi, Zhenfei Yin, Zhipin Wang
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents.
1 code implementation • 5 Nov 2023 • Zhelun Shi, Zhipin Wang, Hongxing Fan, Zhenfei Yin, Lu Sheng, Yu Qiao, Jing Shao
We will publicly release all the detailed implementations for further analysis, as well as an easy-to-use modular toolkit for the integration of new recipes and models, so that ChEF can be a growing evaluation framework for the MLLM community.
1 code implementation • NeurIPS 2023 • Zhenfei Yin, Jiong Wang, JianJian Cao, Zhelun Shi, Dingning Liu, Mukai Li, Lu Sheng, Lei Bai, Xiaoshui Huang, Zhiyong Wang, Jing Shao, Wanli Ouyang
To the best of our knowledge, we present one of the very first open-source endeavors in the field, LAMM, encompassing a Language-Assisted Multi-Modal instruction tuning dataset, framework, and benchmark.
2 code implementations • 18 Mar 2021 • Buyu Li, Yongchi Zhao, Zhelun Shi, Lu Sheng
In this paper, we reformulate it by a two-stage process, ie, a key pose generation and then an in-between parametric motion curve prediction, where the key poses are easier to be synchronized with the music beats and the parametric curves can be efficiently regressed to render fluent rhythm-aligned movements.