no code implementations • 3 Jul 2023 • Xiaoyu Shi, Shurong Chai, Yinhao Li, Jingliang Cheng, Jie Bai, Guohua Zhao, Yen-Wei Chen
However, for medical images with small dataset sizes, deep learning methods struggle to achieve better results on real-world image datasets.
1 code implementation • 22 Jun 2023 • Shurong Chai, Rahul Kumar Jain, Shiyu Teng, Jiaqing Liu, Yinhao Li, Tomoko Tateyama, Yen-Wei Chen
Currently, ongoing research focuses on exploring the effective utilization of these generalized models for specific domains, such as medical imaging.
no code implementations • 9 Apr 2023 • Yinhao Li, Jinrong Yang, Jianjian Sun, Han Bao, Zheng Ge, Li Xiao
Bounded by the inherent ambiguity of depth perception, contemporary multi-view 3D object detection methods fall into the performance bottleneck.
1 code implementation • 26 Oct 2022 • Hongyi Wang, Lanfen Lin, Hongjie Hu, Qingqing Chen, Yinhao Li, Yutaro Iwamoto, Xian-Hua Han, Yen-Wei Chen, Ruofeng Tong
The framework contains two sub-tasks, of which semantic segmentation is the main task and super resolution is an auxiliary task aiding in rebuilding the high frequency information from the LR input.
3 code implementations • 21 Sep 2022 • Yinhao Li, Han Bao, Zheng Ge, Jinrong Yang, Jianjian Sun, Zeming Li
To this end, we introduce an effective temporal stereo method to dynamically select the scale of matching candidates, enable to significantly reduce computation overhead.
Ranked #12 on
3D Object Detection
on nuScenes Camera Only
no code implementations • 22 Aug 2022 • Zengran Wang, Chen Min, Zheng Ge, Yinhao Li, Zeming Li, Hongyu Yang, Di Huang
Instead of using a sole monocular depth method, in this work, we propose a novel Surround-view Temporal Stereo (STS) technique that leverages the geometry correspondence between frames across time to facilitate accurate depth learning.
2 code implementations • 21 Jun 2022 • Yinhao Li, Zheng Ge, Guanyi Yu, Jinrong Yang, Zengran Wang, Yukang Shi, Jianjian Sun, Zeming Li
In this research, we propose a new 3D object detector with a trustworthy depth estimation, dubbed BEVDepth, for camera-based Bird's-Eye-View (BEV) 3D object detection.
Ranked #4 on
3D Object Detection
on Rope3D
no code implementations • 20 Mar 2021 • Li Xiao, Yinhao Li, Luxi Qv, Xinxia Tian, Yijie Peng, S. Kevin Zhou
Segmentation of pathological images is essential for accurate disease diagnosis.
1 code implementation • 16 Dec 2020 • Pengbo Liu, Hu Han, Yuanqi Du, Heqin Zhu, Yinhao Li, Feng Gu, Honghu Xiao, Jun Li, Chunpeng Zhao, Li Xiao, Xinbao Wu, S. Kevin Zhou
Due to the lack of a large-scale pelvic CT dataset with annotations, deep learning methods are not fully explored.
no code implementations • 16 Oct 2020 • Yinhao Li, Yutaro Iwamoto, Lanfen Lin, Rui Xu, Yen-Wei Chen
We construct a parallel connection structure based on the group convolution and feature aggregation to build a 3D CNN that is as wide as possible with few parameters.
no code implementations • 27 Aug 2020 • Yutaro Iwamoto, Kyohei Takeda, Yinhao Li, Akihiko Shiino, Yen-Wei Chen
Deep learning techniques have led to state-of-the-art image super resolution with natural images.
no code implementations • 28 Jun 2020 • Chunlong Luo, Tianqi Yu, Yufan Luo, Manqing Wang, Fuhai Yu, Yinhao Li, Chan Tian, Jie Qiao, Li Xiao
Previous methods only classify manually segmented single chromosome, which is far from clinical practice.
no code implementations • 12 Oct 2019 • Li Xiao, Chunlong Luo, Tianqi Yu, Yufan Luo, Manqing Wang, Fuhai Yu, Yinhao Li, Chan Tian, Jie Qiao
Chromosome enumeration is an essential but tedious procedure in karyotyping analysis.
no code implementations • 11 Oct 2019 • Bin Qian, Jie Su, Zhenyu Wen, Devki Nandan Jha, Yinhao Li, Yu Guan, Deepak Puthal, Philip James, Renyu Yang, Albert Y. Zomaya, Omer Rana, Lizhe Wang, Maciej Koutny, Rajiv Ranjan
Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock complete potentials of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services.
no code implementations • 10 May 2018 • Yinhao Li, Awa Alqahtani, Ellis Solaiman, Charith Perera, Prem Prakash Jayaraman, Boualem Benatallah, Rajiv Ranjan
Within the rapidly developing Internet of Things (IoT), numerous and diverse physical devices, Edge devices, Cloud infrastructure, and their quality of service requirements (QoS), need to be represented within a unified specification in order to enable rapid IoT application development, monitoring, and dynamic reconfiguration.