no code implementations • 20 Mar 2024 • Jianhao Xie, Ruofan Liao, Ziang Zhang, Sida Yi, Yuesheng Zhu, Guibo Luo
To address these issues, we propose a segmentation model based on Prompt-Mamba, which incorporates the latest Vision-Mamba and prompt technologies.
no code implementations • 11 Mar 2024 • Jianhao Xie, Ziang Zhang, Guibo Luo, Yuesheng Zhu
Large pre-trained models with their numerous model parameters and extensive training datasets have shown excellent performance in various tasks.
no code implementations • 23 Oct 2023 • Zhibo Xing, Zijian Zhang, Jiamou Liu, Ziang Zhang, Meng Li, Liehuang Zhu, Giovanni Russello
However, in practice, due to various challenges such as limited computational resources and data privacy concerns, users in need of models often cannot train machine learning models locally.
1 code implementation • 13 Oct 2023 • Zehan Wang, Ziang Zhang, Luping Liu, Yang Zhao, Haifeng Huang, Tao Jin, Zhou Zhao
Inspired by recent C-MCR, this paper proposes Extending Multimodal Contrastive Representation (Ex-MCR), a training-efficient and paired-data-free method to flexibly learn unified contrastive representation space for more than three modalities by integrating the knowledge of existing MCR spaces.
1 code implementation • 17 Aug 2023 • Zehan Wang, Haifeng Huang, Yang Zhao, Ziang Zhang, Zhou Zhao
This paper presents Chat-3D, which combines the 3D visual perceptual ability of pre-trained 3D representations and the impressive reasoning and conversation capabilities of advanced LLMs to achieve the first universal dialogue systems for 3D scenes.
no code implementations • NeurIPS 2023 • Zehan Wang, Yang Zhao, Xize Cheng, Haifeng Huang, Jiageng Liu, Li Tang, Linjun Li, Yongqi Wang, Aoxiong Yin, Ziang Zhang, Zhou Zhao
This paper proposes a novel training-efficient method for learning MCR without paired data called Connecting Multi-modal Contrastive Representations (C-MCR).