no code implementations • 21 Sep 2022 • Sangyun Shin, Stuart Golodetz, Madhu Vankadari, Kaichen Zhou, Andrew Markham, Niki Trigoni
Supervised approaches typically require the annotation of large training sets; there has thus been great interest in leveraging weakly, semi- or self-supervised methods to avoid this, with much success.
1 code implementation • 14 Sep 2022 • Kaichen Zhou, Lanqing Hong, Changhao Chen, Hang Xu, Chaoqiang Ye, Qingyong Hu, Zhenguo Li
Self-supervised depth learning from monocular images normally relies on the 2D pixel-wise photometric relation between temporally adjacent image frames.
1 code implementation • CVPR 2022 • Jia-Xing Zhong, Kaichen Zhou, Qingyong Hu, Bing Wang, Niki Trigoni, Andrew Markham
Scene flow is a powerful tool for capturing the motion field of 3D point clouds.
no code implementations • 13 Sep 2021 • Kaichen Zhou, Lanqing Hong, Shoukang Hu, Fengwei Zhou, Binxin Ru, Jiashi Feng, Zhenguo Li
In view of these, we propose DHA, which achieves joint optimization of Data augmentation policy, Hyper-parameter and Architecture.
no code implementations • 1 Jan 2021 • Kaichen Zhou, Lanqing Hong, Fengwei Zhou, Binxin Ru, Zhenguo Li, Trigoni Niki, Jiashi Feng
Our method performs co-optimization of the neural architectures, training hyper-parameters and data augmentation policies in an end-to-end fashion without the need of model retraining.
no code implementations • 26 Jun 2020 • Chengliang Dai, Shuo Wang, Yuanhan Mo, Kaichen Zhou, Elsa Angelini, Yike Guo, Wenjia Bai
Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks.
no code implementations • 12 Mar 2020 • Kaichen Zhou, Changhao Chen, Bing Wang, Muhamad Risqi U. Saputra, Niki Trigoni, Andrew Markham
We conjecture that this is because of the naive approaches to feature space fusion through summation or concatenation which do not take into account the different strengths of each modality.
no code implementations • 6 Mar 2020 • Kaichen Zhou, Shiji Song, Gao Huang, Wu Cheng, Quan Zhou
Specifically, the proposed algorithm can be used to estimate the upper and lower bounds of the updated classifier's coefficient matrix with a low computational complexity related to the size of the updated dataset.
no code implementations • 6 Mar 2020 • Kaichen Zhou, Shiji Song, Anke Xue, Keyou You, Hui Wu
Then we develop two algorithms for optimizing the energy efficiency of train operation.