no code implementations • 14 Mar 2024 • Yuxuan Cai, Xinwei He, Dingkang Liang, Ao Tong, Xiang Bai
Recently, large vision and language models have shown their success when adapting them to many downstream tasks.
1 code implementation • 7 Mar 2024 • 01. AI, :, Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, Kaidong Yu, Peng Liu, Qiang Liu, Shawn Yue, Senbin Yang, Shiming Yang, Tao Yu, Wen Xie, Wenhao Huang, Xiaohui Hu, Xiaoyi Ren, Xinyao Niu, Pengcheng Nie, Yuchi Xu, Yudong Liu, Yue Wang, Yuxuan Cai, Zhenyu Gu, Zhiyuan Liu, Zonghong Dai
The Yi model family is based on 6B and 34B pretrained language models, then we extend them to chat models, 200K long context models, depth-upscaled models, and vision-language models.
1 code implementation • 11 Oct 2023 • Yuxuan Cai, Dingkang Liang, Dongliang Luo, Xinwei He, Xin Yang, Xiang Bai
To alleviate this issue, we present a Discrepancy Aware Framework (DAF), which demonstrates robust performance consistently with simple and cheap strategies across different anomaly detection benchmarks.
1 code implementation • NeurIPS 2023 • Qi Han, Yuxuan Cai, Xiangyu Zhang
Such design enables our architecture with the nice property: maintaining disentangled low-level and semantic information at the end of the network in MIM pre-training.
1 code implementation • 22 Dec 2022 • Yuxuan Cai, Yizhuang Zhou, Qi Han, Jianjian Sun, Xiangwen Kong, Jun Li, Xiangyu Zhang
Such architectural scheme attributes RevCol very different behavior from conventional networks: during forward propagation, features in RevCol are learned to be gradually disentangled when passing through each column, whose total information is maintained rather than compressed or discarded as other network does.
Ranked #8 on Semantic Segmentation on ADE20K (using extra training data)
no code implementations • 22 Nov 2021 • Yifan Gong, Geng Yuan, Zheng Zhan, Wei Niu, Zhengang Li, Pu Zhao, Yuxuan Cai, Sijia Liu, Bin Ren, Xue Lin, Xulong Tang, Yanzhi Wang
Weight pruning is an effective model compression technique to tackle the challenges of achieving real-time deep neural network (DNN) inference on mobile devices.
no code implementations • 28 Jun 2021 • Pu Zhao, Wei Niu, Geng Yuan, Yuxuan Cai, Bin Ren, Yanzhi Wang, Xue Lin
Object detection plays an important role in self-driving cars for security development.
no code implementations • 16 Jun 2021 • Geng Yuan, Zhiheng Liao, Xiaolong Ma, Yuxuan Cai, Zhenglun Kong, Xuan Shen, Jingyan Fu, Zhengang Li, Chengming Zhang, Hongwu Peng, Ning Liu, Ao Ren, Jinhui Wang, Yanzhi Wang
More importantly, our method does not require extra hardware cost compared to the traditional two-column mapping scheme.
no code implementations • 26 Dec 2020 • Pu Zhao, Wei Niu, Geng Yuan, Yuxuan Cai, Hsin-Hsuan Sung, Sijia Liu, Xipeng Shen, Bin Ren, Yanzhi Wang, Xue Lin
3D object detection is an important task, especially in the autonomous driving application domain.
no code implementations • CVPR 2021 • Zhengang Li, Geng Yuan, Wei Niu, Pu Zhao, Yanyu Li, Yuxuan Cai, Xuan Shen, Zheng Zhan, Zhenglun Kong, Qing Jin, Zhiyu Chen, Sijia Liu, Kaiyuan Yang, Bin Ren, Yanzhi Wang, Xue Lin
With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed.
3 code implementations • 12 Sep 2020 • Yuxuan Cai, Hongjia Li, Geng Yuan, Wei Niu, Yanyu Li, Xulong Tang, Bin Ren, Yanzhi Wang
In this work, we propose YOLObile framework, a real-time object detection on mobile devices via compression-compilation co-design.