no code implementations • 9 Apr 2024 • Yi Gui, Zhen Li, Yao Wan, Yemin Shi, Hongyu Zhang, Yi Su, Shaoling Dong, Xing Zhou, Wenbin Jiang
Automatically generating UI code from webpage design visions can significantly alleviate the burden of developers, enabling beginner developers or designers to directly generate Web pages from design diagrams.
no code implementations • 20 Mar 2024 • Jingkun An, Yinghao Zhu, Zongjian Li, Haoran Feng, Bohua Chen, Yemin Shi, Chengwei Pan
Text-to-Image (T2I) diffusion models have achieved remarkable success in image generation.
1 code implementation • 29 Sep 2023 • Guangyao Chen, Siwei Dong, Yu Shu, Ge Zhang, Jaward Sesay, Börje F. Karlsson, Jie Fu, Yemin Shi
Therefore, we introduce AutoAgents, an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks.
1 code implementation • 30 Aug 2023 • Yu Shu, Siwei Dong, Guangyao Chen, Wenhao Huang, Ruihua Zhang, Daochen Shi, Qiqi Xiang, Yemin Shi
In this work, we propose Large Language and Speech Model (LLaSM).
1 code implementation • 31 May 2023 • Yizhi Li, Ruibin Yuan, Ge Zhang, Yinghao Ma, Xingran Chen, Hanzhi Yin, Chenghao Xiao, Chenghua Lin, Anton Ragni, Emmanouil Benetos, Norbert Gyenge, Roger Dannenberg, Ruibo Liu, Wenhu Chen, Gus Xia, Yemin Shi, Wenhao Huang, Zili Wang, Yike Guo, Jie Fu
Although SSL has been proven effective in speech and audio, its application to music audio has yet to be thoroughly explored.
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/}}.
no code implementations • 24 Aug 2021 • Gang Yu, Zhongzhi Yu, Yemin Shi, Yingshuo Wang, Xiaoqing Liu, Zheming Li, Yonggen Zhao, Fenglei Sun, Yizhou Yu, Qiang Shu
The first stage structuralizes test results by extracting relevant numerical values from clinical notes, and the disease identification stage provides a diagnosis based on text-form clinical notes and the structured data obtained from the first stage.
2 code implementations • CVPR 2021 • Jichang Li, Guanbin Li, Yemin Shi, Yizhou Yu
Pseudo labeling expands the number of ``labeled" samples in each class in the target domain, and thus produces a more robust and powerful cluster core for each class to facilitate adversarial learning.
1 code implementation • ECCV 2020 • Guangyao Chen, Limeng Qiao, Yemin Shi, Peixi Peng, Jia Li, Tiejun Huang, ShiLiang Pu, Yonghong Tian
In this process, one of the key challenges is to reduce the risk of generalizing the inherent characteristics of numerous unknown samples learned from a small amount of known data.
no code implementations • 11 Mar 2020 • Zhongzhi Yu, Yemin Shi, Tiejun Huang, Yizhou Yu
Thus, KQ can represent the weight tensor in the convolution layer with low-bit indexes and a kernel codebook with limited size, which enables KQ to achieve significant compression ratio.
no code implementations • ICCV 2019 • Limeng Qiao, Yemin Shi, Jia Li, Yao-Wei Wang, Tiejun Huang, Yonghong Tian
By solving the problem with its closed-form solution on the fly with the setup of transduction, our approach efficiently tailors an episodic-wise metric for each task to adapt all features from a shared task-agnostic embedding space into a more discriminative task-specific metric space.
no code implementations • 6 May 2019 • Yu Shu, Yemin Shi, Yao-Wei Wang, Tiejun Huang, Yonghong Tian
Predictors for new categories are added to the classification layer to "open" the deep neural networks to incorporate new categories dynamically.
no code implementations • 23 Jan 2019 • Yu Shu, Yemin Shi, Yao-Wei Wang, Yixiong Zou, Qingsheng Yuan, Yonghong Tian
Most of the existing action recognition works hold the \textit{closed-set} assumption that all action categories are known beforehand while deep networks can be well trained for these categories.
no code implementations • 16 Nov 2016 • Yemin Shi, Yonghong Tian, Yao-Wei Wang, Tiejun Huang
We also introduce an attention mechanism on the temporal domain to capture the long-term dependence meanwhile finding the salient portions.
1 code implementation • ICCV 2017 • Yemin Shi, Yonghong Tian, Yao-Wei Wang, Tiejun Huang
Despite a lot of research efforts devoted in recent years, how to efficiently learn long-term dependencies from sequences still remains a pretty challenging task.
no code implementations • 10 Sep 2016 • Yemin Shi, Yonghong Tian, Yao-Wei Wang, Tiejun Huang
Nevertheless, most of the existing features or descriptors cannot capture motion information effectively, especially for long-term motion.