no code implementations • 7 Mar 2024 • Zhaoqun Li, Jingcheng Yu, Qiwei Ye
Deep learning has made significant progress in protein structure prediction, advancing the development of computational biology.
no code implementations • 20 Feb 2024 • Yizhi Li, Ge Zhang, Xingwei Qu, Jiali Li, Zhaoqun Li, Zekun Wang, Hao Li, Ruibin Yuan, Yinghao Ma, Kai Zhang, Wangchunshu Zhou, Yiming Liang, Lei Zhang, Lei Ma, Jiajun Zhang, Zuowen Li, Stephen W. Huang, Chenghua Lin, Wenhu Chen, Jie Fu
The advancement of large language models (LLMs) has enhanced the ability to generalize across a wide range of unseen natural language processing (NLP) tasks through instruction-following.
2 code implementations • 17 Apr 2023 • Ge Zhang, Yemin Shi, Ruibo Liu, Ruibin Yuan, Yizhi Li, Siwei Dong, Yu Shu, Zhaoqun Li, Zekun Wang, Chenghua Lin, Wenhao Huang, Jie Fu
Instruction tuning is widely recognized as a key technique for building generalist language models, which has attracted the attention of researchers and the public with the release of InstructGPT~\citep{ouyang2022training} and ChatGPT\footnote{\url{https://chat. openai. com/}}.
1 code implementation • 4 Oct 2021 • Zhaoqun Li, Xu Liang, Dandan Fan, Jinxing Li, David Zhang
Bimodal palmprint recognition leverages palmprint and palm vein images simultaneously, which achieves high accuracy by multi-model information fusion and has strong anti-falsification property.
no code implementations • 3 Mar 2021 • Zhaoqun Li, Xu Liang, Dandan Fan, Jinxing Li, Wei Jia, David Zhang
To our best knowledge, it is the largest contactless palmprint image benchmark ever collected with regard to the number of individuals and palms.
no code implementations • 10 Dec 2020 • Zhaoqun Li, Hongren Wang, Jinxing Li
In 3D shape recognition, multi-view based methods leverage human's perspective to analyze 3D shapes and have achieved significant outcomes.
no code implementations • 16 Nov 2020 • Zhaoqun Li
To make up the gap, in this paper, we propose a novel regularization term called Gram regularization which reinforces the learning ability of the network by encouraging the weight kernels to extract different information on the corresponding feature map.
no code implementations • 3 Jun 2019 • Zhaoqun Li, Cheng Xu, Biao Leng
In this paper, we propose the Collaborative Inner Product Loss (CIP Loss) to obtain ideal shape embedding that discriminative among different categories and clustered within the same class.
no code implementations • 21 Nov 2018 • Zhaoqun Li, Cheng Xu, Biao Leng
How to obtain the desirable representation of a 3D shape, which is discriminative across categories and polymerized within classes, is a significant challenge in 3D shape retrieval.