1 code implementation • 26 Oct 2023 • Zhaoyang Liu, Zeqiang Lai, Zhangwei Gao, Erfei Cui, Ziheng Li, Xizhou Zhu, Lewei Lu, Qifeng Chen, Yu Qiao, Jifeng Dai, Wenhai Wang
We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving complex real-world tasks.
1 code implementation • 8 Jun 2023 • Changyao Tian, Chenxin Tao, Jifeng Dai, Hao Li, Ziheng Li, Lewei Lu, Xiaogang Wang, Hongsheng Li, Gao Huang, Xizhou Zhu
In each denoising step, our method first decodes pixels from previous VQ tokens, then generates new VQ tokens from the decoded pixels.
no code implementations • 2 Jun 2023 • Zeqiang Lai, Yuchen Duan, Jifeng Dai, Ziheng Li, Ying Fu, Hongsheng Li, Yu Qiao, Wenhai Wang
In this paper, we propose to ameliorate the semantic segmentation quality of existing discriminative approaches with a mask prior modeled by a recently-developed denoising diffusion generative model.
1 code implementation • 16 May 2023 • Ziheng Li, Shaohan Huang, Zihan Zhang, Zhi-Hong Deng, Qiang Lou, Haizhen Huang, Jian Jiao, Furu Wei, Weiwei Deng, Qi Zhang
Recent studies have shown that dual encoder models trained with the sentence-level translation ranking task are effective methods for cross-lingual sentence embedding.
no code implementations • 23 Feb 2023 • Ziheng Li, Shibo Jie, Zhi-Hong Deng
In continual learning, model needs to continually learn a feature extractor and classifier on a sequence of tasks.
no code implementations • 19 May 2022 • Gehui Shen, Shibo Jie, Ziheng Li, Zhi-Hong Deng
In our framework, a generative classifier which utilizes replay memory is used for inference, and the training objective is a pair-based metric learning loss which is proven theoretically to optimize the feature space in a generative way.
1 code implementation • 22 Apr 2022 • Shibo Jie, Zhi-Hong Deng, Ziheng Li
We study a practical setting of continual learning: fine-tuning on a pre-trained model continually.
no code implementations • 10 Mar 2019 • Ziheng Li, Wenkun Zhang, Linyuan Wang, Ailong Cai, Ningning Liang, Bin Yan, Lei LI
Limited-angle computed tomography (CT) image reconstruction is a challenging reconstruction problem in the fields of CT. With the development of deep learning, the generative adversarial network (GAN) perform well in image restoration by approximating the distribution of training sample data.
Medical Physics