Multimodal Vision Research Laboratory

MVRL

Research Area: Uncertainty Estimation

We are interested in developing novel algorithms for uncertainty estimation in computer vision. Our research includes improved trainable calibration methods for neural networks, neural network calibration for medical imaging classification using DCA regularization, and neural network decision-making criteria consistency analysis via input sensitivity. We focus on developing methods that provide reliable uncertainty estimates, particularly for applications in medical imaging and other safety-critical domains where accurate confidence measures are essential.

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Publications

  1. Xing E, Xing X, Liu L, Jacobs N, Qu Y, Liang G. 2022. Neural Network Decision-Making Criteria Consistency Analysis via Inputs Sensitivity. In: International Conference on Pattern Recognition (ICPR 2022). DOI: 10.1109/ICPR56361.2022.9956394.
    doi bibtex Uncertainty
  2. Thumbnail for Improved Trainable Calibration Method for Neural Networks
    Liang G, Zhang Y, Wang X, Jacobs N. 2020. Improved Trainable Calibration Method for Neural Networks. In: British Machine Vision Conference (BMVC).
    website paper bibtex Medical Uncertainty
  3. Liang G, Zhang Y, Jacobs N. 2020. Neural Network Calibration for Medical Imaging Classification Using DCA Regularization. In: ICML 2020 workshop on Uncertainty and Robustness in Deep Learning (UDL).
    paper bibtex Medical Uncertainty