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Continual Learning: learn new knowledge incrementally while not forgetting the old knowledge.

  • Online continual learning: Learn new knowledge incrementally from data stream where each data is observed only once by the model
  • Unsupervised/Self-supervised continual learning: Learn new knowledge or feature representation incrementally from unlabeled data
  • Continual learning in real world applications: Apply continual learning in various real-world applications such as food recognition

    Image-based Dietary Assessment: Develop robust algorithms to automate the process of dietary assessment by using eating occasion images
  • Food recognition: Identify food type from eating occasion images in various scenarios and environments
  • Food portion size estimation: Estimate food energy/volume directly from eating occasion images
  • Food image generation: Generate synthetic and diverse food images using generative models
  • 3D food data: Collect 3D food model and apply it for various 3D related downstream tasks

    Long-tailed Learning: Learn knowledge from severe class-imbalanced data
  • Long-tailed classification: Learn unbiased feature representation and classifier for image classification
  • Long-tailed continual learning: Integrate long-tailed learning with continual learning to incrementally learn new knowledge from class-imbalanced data

    Laparoscopic Image Analysis for Minimally-Invasive Surgery
  • Semi/Weakly/Self-Supervised Semantic Segmentation: Learn to segment different organs in laparoscopic surgery image/video with limited labeled data
  • Human-in-the-loop Learning: Incorporate surgeon's expertise in real-time to refine the model's performance during the live surgeries

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    Last Updated: 01/21/2024 13:41:55
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