Search Results for author: Rosa H. M. Chan

Found 7 papers, 1 papers with code

Tracking Fast by Learning Slow: An Event-based Speed Adaptive Hand Tracker Leveraging Knowledge in RGB Domain

no code implementations28 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.

OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning

2 code implementations15 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.

Object Object Recognition

An Efficient and Flexible Spike Train Model via Empirical Bayes

no code implementations10 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.

Bayesian Inference

Mutual Information-Based Unsupervised Feature Transformation for Heterogeneous Feature Subset Selection

no code implementations24 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.

feature selection

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