no code implementations • 28 Feb 2023 • Chuanlin Lan, Ziyuan Yin, Arindam Basu, Rosa H. M. Chan
To realize our solution, we constructed the first 3D hand tracking dataset captured by an event camera in a real-world environment, figured out two data augment methods to narrow the domain gap between slow and fast motion data, developed a speed adaptive event stream segmentation method to handle hand movements in different moving speeds, and introduced a new event-to-frame representation method adaptive to event streams with different lengths.
no code implementations • 27 Jul 2021 • Alessio Xompero, Santiago Donaher, Vladimir Iashin, Francesca Palermo, Gökhan Solak, Claudio Coppola, Reina Ishikawa, Yuichi Nagao, Ryo Hachiuma, Qi Liu, Fan Feng, Chuanlin Lan, Rosa H. M. Chan, Guilherme Christmann, Jyun-Ting Song, Gonuguntla Neeharika, Chinnakotla Krishna Teja Reddy, Dinesh Jain, Bakhtawar Ur Rehman, Andrea Cavallaro
In this paper, we present a range of methods and an open framework to benchmark acoustic and visual perception for the estimation of the capacity of a container, and the type, mass, and amount of its content.
no code implementations • 26 Apr 2020 • Qi She, Fan Feng, Qi Liu, Rosa H. M. Chan, Xinyue Hao, Chuanlin Lan, Qihan Yang, Vincenzo Lomonaco, German I. Parisi, Heechul Bae, Eoin Brophy, Baoquan Chen, Gabriele Graffieti, Vidit Goel, Hyonyoung Han, Sathursan Kanagarajah, Somesh Kumar, Siew-Kei Lam, Tin Lun Lam, Liang Ma, Davide Maltoni, Lorenzo Pellegrini, Duvindu Piyasena, ShiLiang Pu, Debdoot Sheet, Soonyong Song, Youngsung Son, Zhengwei Wang, Tomas E. Ward, Jianwen Wu, Meiqing Wu, Di Xie, Yangsheng Xu, Lin Yang, Qiaoyong Zhong, Liguang Zhou
This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top $8$ finalists (out of over~$150$ teams).
2 code implementations • 15 Nov 2019 • Qi She, Fan Feng, Xinyue Hao, Qihan Yang, Chuanlin Lan, Vincenzo Lomonaco, Xuesong Shi, Zhengwei Wang, Yao Guo, Yimin Zhang, Fei Qiao, Rosa H. M. Chan
Yet, robotic vision poses unique challenges for applying visual algorithms developed from these standard computer vision datasets due to their implicit assumption over non-varying distributions for a fixed set of tasks.
no code implementations • 13 Nov 2019 • Xuesong Shi, Dongjiang Li, Pengpeng Zhao, Qinbin Tian, Yuxin Tian, Qiwei Long, Chunhao Zhu, Jingwei Song, Fei Qiao, Le Song, Yangquan Guo, Zhigang Wang, Yimin Zhang, Baoxing Qin, Wei Yang, Fangshi Wang, Rosa H. M. Chan, Qi She
We also design benchmarking metrics for lifelong SLAM, with which the robustness and accuracy of pose estimation are evaluated separately.
no code implementations • 10 May 2016 • Qi She, Xiaoli Wu, Beth Jelfs, Adam S. Charles, Rosa H. M. Chan
Our method integrates both Generalized Linear Models (GLMs) and empirical Bayes theory, which aims to (1) improve the accuracy and reliability of parameter estimation, compared to the maximum likelihood-based method for NB-GLM and Poisson-GLM; (2) effectively capture the over-dispersion nature of spike counts from both simulated data and experimental data; and (3) provide insight into both neural interactions and spiking behaviours of the neuronal populations.
no code implementations • 24 Nov 2014 • Min Wei, Tommy W. S. Chow, Rosa H. M. Chan
Conventional mutual information (MI) based feature selection (FS) methods are unable to handle heterogeneous feature subset selection properly because of data format differences or estimation methods of MI between feature subset and class label.