1 code implementation • CVPR 2022 • Xiuchao Sui, Shaohua Li, Xue Geng, Yan Wu, Xinxing Xu, Yong liu, Rick Goh, Hongyuan Zhu
This is mainly because the correlation volume, the basis of pixel matching, is computed as the dot product of the convolutional features of the two images.
Ranked #9 on Optical Flow Estimation on KITTI 2015 (train)
1 code implementation • 10 Jul 2021 • Shaohua Li, Xiuchao Sui, Jie Fu, Huazhu Fu, Xiangde Luo, Yangqin Feng, Xinxing Xu, Yong liu, Daniel Ting, Rick Siow Mong Goh
Thus, the chance of overfitting the annotations is greatly reduced, and the model can perform robustly on the target domain after being trained on a few annotated images.
1 code implementation • 20 May 2021 • Shaohua Li, Xiuchao Sui, Xiangde Luo, Xinxing Xu, Yong liu, Rick Goh
Medical image segmentation is important for computer-aided diagnosis.
Ranked #1 on Brain Tumor Segmentation on BRATS 2019
1 code implementation • 12 Apr 2020 • Shaohua Li, Xiuchao Sui, Jie Fu, Yong liu, Rick Siow Mong Goh
To make CNNs more invariant to transformations, we propose "Feature Lenses", a set of ad-hoc modules that can be easily plugged into a trained model (referred to as the "host model").
no code implementations • 11 Dec 2019 • Tianying Wang, Wei Qi Toh, Hao Zhang, Xiuchao Sui, Shaohua Li, Yong liu, Wei Jing
The proposed RoboCoDraw system takes a real human face image as input, converts it to a stylized avatar, then draws it with a robotic arm.
Robotics Graphics
no code implementations • 4 Jul 2019 • Shaohua Li, Yong liu, Xiuchao Sui, Cheng Chen, Gabriel Tjio, Daniel Shu Wei Ting, Rick Siow Mong Goh
Deep learning for medical image classification faces three major challenges: 1) the number of annotated medical images for training are usually small; 2) regions of interest (ROIs) are relatively small with unclear boundaries in the whole medical images, and may appear in arbitrary positions across the x, y (and also z in 3D images) dimensions.